In order to effectively improve the accuracy and comprehensiveness of evaluation results, a method for evaluating the effectiveness of online interactive teaching under social network analysis is proposed. Firstly, collect online interactive teaching data and use Pearson correlation coefficient to calculate the correlation coefficient between the data, filtering out indicators significantly related to teaching effectiveness. Secondly, use information entropy to calculate the weight of evaluation indicators. Finally, construct a social network model, measure the node intimacy function, and identify important nodes in the social network to optimise the social network model and more accurately evaluate teaching effectiveness. The experimental results show that the highest accuracy value of the method proposed in this paper is 95%, the highest precision value is 72%, and the highest recall value is 92%, all of which are better than existing methods, fully demonstrating the effectiveness of its teaching effectiveness evaluation.
In order to solve the problems of low internal risk identification rate, low accuracy of internal control strategy execution, and long response time in traditional methods, an optimisation method of internal control strategies in enterprises under the background of digital empowerment is proposed. Analysing the impact of digital empowerment on internal control in enterprises, identifying the problems existing in internal control, and proposing an optimisation path for internal control strategies from the perspectives of clarifying and integrating strategic objectives and internal control processes, accurately identifying and monitoring key control points (KCPs) in real time, balancing cost-effectiveness and risks, tailoring and continuously optimising internal control systems, and comprehensively optimising other key measures for internal control. The test results show that the maximum internal risk identification rate of this method is 97.8%, the maximum accuracy of internal control strategy execution is 97.7%, and the average response time is 37.09 min.
To address the challenges associated with sluggish inventory circulation, elevated product return percentages, and extended supply chain reaction durations within conventional approaches, an innovation method of cross border e-commerce supply chain management mechanism under digital background is proposed. After analysing the structure of cross-border e-commerce supply chain, based on the reliability and collaboration of the supply chain system, innovative strategies for cross-border e-commerce supply chain management mechanism under the digital background were analysed, including data-driven decision optimisation, digital upgrading of supplier management, intelligent inventory management, digital and intelligent logistics management, technological innovation and digital application, as well as risk management and emergency response. The findings from the trials indicate that the turnover ratio for inventory using the new technique fluctuates between 20.6% and 30.1%. Meanwhile, the typical rate of returns stands at 1.57%, and the supply chain's average reaction period is 12.64 days. The practical implementation has yielded positive outcomes.
This paper proposes a logistics multi-level supply chain transportation scheduling method based on an improved fruit fly algorithm to address the problems of high transportation costs and low transportation efficiency in logistics multi-level supply chain transportation scheduling methods. Firstly, clarify the research scope and objectives by setting objective functions and constraints. Secondly, information exchange, mutation strategy, and probabilistic flight strategy were introduced to improve the fruit fly algorithm. Finally, based on the set algorithm parameters, use the improved Drosophila algorithm to evaluate the fitness of various logistics transportation plans and output the optimal results. After experimental verification, the total transportation time of the logistics transportation plan designed in this paper is 854 min, with 8 dispatched vehicles and a corresponding fuel cost of 2140.31 yuan. This method can effectively reduce transportation costs, improve logistics transportation efficiency, and has certain practical application value.
To address the problems of poor adaptability of layout evolution parameters, poor convergence of constraint conditions, and high deviation in coordination optimisation in traditional methods, an evolution and coordination optimisation method of regional innovation and entrepreneurship space layout under the background of social networks has been designed. Determine the impact mechanism and layout constraints of the evolution of regional innovation and entrepreneurship spatial layout, and combine social networks to complete the analysis of the evolution of regional innovation and entrepreneurship spatial layout. Construct a spatial topology diagram for coordinating and optimising the layout of regional innovation and entrepreneurship spaces, determine the fitness of regional spatial layout parameters, and combine the coordination optimisation model to achieve coordinated optimisation of new entrepreneurial space layout parameters. The experimental findings indicate that the proposed method has good adaptability of the layout evolution parameters, good convergence of constraint conditions, low deviation in coordinated optimisation.
The research on mining hotspot topic in online public opinion is of great significance for improving social management efficiency and promoting economic development. In order to overcome the problems of low accuracy, low recall, and long response time in traditional methods, a network public opinion hotspot topic mining method based on improved support vector machine (SVM) is proposed. Utilise distributed web crawlers to collect network public opinion data, and extract features of the collected network public opinion data through adaptive domain relationships. Introduce the least squares method to improve the SVM, input the feature extraction results into the improved SVM, and obtain the mining results of network public opinion hotspot topic. The experimental results show that the accuracy of network public opinion hotspot topic mining using this method varies between 96.3% and 98.3%, with an average recall rate of 97.7% and a response time of 5.9 s.
In order to solve the problems of low system integrity, poor closeness and reliability of evaluation indicators in traditional methods, a construction method of teaching service quality evaluation index system under the digital background is proposed. Complete the selection of evaluation indicator data through the ripple effect model. By calculating the Min's distance to measure the similarity of evaluation index data and removing data with high similarity, principal component analysis (PCA) is used to normalise and reduce the dimensionality of the data. Using extreme learning machine algorithm to classify and process evaluation indicators, and achieving research on the construction of teaching service quality evaluation indicator system. The case analysis results show that when the number of indicators is 1000, the completeness of the indicator system of the proposed method is 98%, the closeness is closer to 1, and the maximum reliability is 97%, which has the characteristics of high feasibility.
To address the issues of elevated inaccuracy levels, prolonged verification durations, and diminished efficacy in risk management associated with conventional approaches, a financial systemic risk in the digital era and its dual pillar regulatory framework construction method are proposed. Analyse the impact of systemic financial risks on financial stability, combined with financial system risk measurement indicators such as CoVaR and Sharply value are used to identify financial system risks in the digital era. Based on the identification results of financial systemic risks in the digital era, a dual pillar regulation framework is constructed to achieve financial systemic risk regulation in the digital era from the perspectives of monetary policy and macro prudential policy. After experimental testing, it was found that the average risk misreporting rate of this method is 3.02%, the recognition time range is 0.21~0.63 s, and the average success rate of risk control is 96.17%.
The research on online financial product marketing information push is of great significance for improving the efficiency of financial institutions, optimising product design, and promoting financial technology innovation and digital transformation. In order to solve the problems existing in current methods, an online financial product marketing information push method based on social relationship network analysis is proposed. This method uses social network to analyse and calculate the influence of user relationship, and combines with two-way gated recurrent unit (GRU) neural network to extract user interest. Push online financial product marketing information based on user interests and multi-Markov chain. Experimental results show that the proposed method performs well in accuracy, push time and user retention rate. Therefore, this method has the characteristics of high precision and high efficiency.
In order to solve the problems of resource utilisation rate, achievement conversion rate, and low student satisfaction in traditional methods, a construction method of innovation and entrepreneurship education ecosystem in universities based on social network analysis is proposed. Analyse the concept of the innovation and entrepreneurship education ecosystem in universities, determine the relationship between the constituent elements, and use social network analysis methods to identify the bottlenecks and shortcomings of the innovation and entrepreneurship education ecosystem in universities. Starting from establishing innovative educational models, strengthening interdisciplinary integration, establishing innovation and entrepreneurship practice platforms, and strengthening industry and enterprise cooperation, will complete the construction of the innovation and entrepreneurship education ecosystem in universities. The test results of the example application show that the average resource utilisation rate of the proposed method is 83.27%, the average achievement conversion rate is 86.55%, and student satisfaction varies between 91.2 and 93.6%.
In order to solve the problems of low mining coverage and low product conversion rate in traditional marketing information dynamic mining methods, a new dynamic mining method of multimedia marketing information for products under the background of data driven is proposed. Using web crawler technology to crawl multimedia marketing information data of products, and extracting data features through sliding clustering. By using fuzzy clustering algorithm to perform fuzzy clustering on data features, a clustering dataset item set is constructed and merged into an item set to assign weights. Combined with association rules, dynamic mining of multimedia marketing information for products is achieved. Experimental results have shown that the mining coverage rate of this method is 91~97%, and the product conversion rate is 19.1% when the data volume is 8000. The mining coverage rate and product conversion rate are both at a high level, and the mining effect is good.
In order to overcome the problems of low quality index, innovative thinking path coefficient, and high error value in traditional methods, a high quality construction method of innovation and entrepreneurship education system from the perspective of three comprehensive education is proposed. Firstly, analyse the participants and influencing factors in the construction of the innovation and entrepreneurship education system from the perspective of comprehensive education. Secondly, establish a preliminary innovation and entrepreneurship education system. Finally, after evaluating the quality of the innovation and entrepreneurship education system, we will optimise and adjust the preliminary construction system to achieve high-quality construction of the innovation and entrepreneurship education system. The experimental results show that the quality index of the innovation and entrepreneurship education system constructed in this article is high, the path coefficient of innovative thinking is high, and the average evaluation error value is low.
In order to optimise the effectiveness of resource recommendation and improve the coverage of personalised learning resource recommendation results, a personalised learning resource online recommendation method based on multidimensional feature extraction is proposed. Firstly, based on the feature expression and density parameters of user behaviour data, cluster the users. Secondly, extract users' time features, preference features, and learning resource features, and use feature matrices for efficient feature mining. Finally, the extracted personalised learning resource features are input into the self-organising maps (SOM) network, and through the resource scoring mechanism and similarity calculation process, recommendation prediction values are generated and sorted to form a personalised recommendation set. The experimental results show that this method can accurately provide resource solutions that meet user needs when the number of resources and users increase, and the recommendation coverage rate always remains above 90%.
