In order to solve the practical problem that the massive video data generated by monitoring equipment cannot be processed efficiently temporarily, this paper proposes a framework for face recognition of massive video data based on distributed environment. Combined with the application features of face recognition, this method designs a strategy for fast reading of massive video data and optimizes the feature data obtained by the cloud platform, so as to speed up the retrieval speed of face features. The results are as follows: the compression rate of the proposed compression method is higher than that of the traditional matrix triple and binary methods, which is increased by about 65%; the data optimization method in this paper greatly reduces the amount of feature data, which is 7.08 times less than that in the nonoptimization state. At the same time, the process of face recognition is reduced from 12.6 seconds to 2.73 seconds, and the time of feature decompression is only 0.75 seconds more than the original; the experiment shows that it takes 10180 seconds for the system to process 200 GB pictures with 9 computing nodes, and the total running time of the system is 4737 seconds longer than that of a single node, accounting for about 5.45% of the total time of a single node system. At the same time, the experimental data show that the system is 8.53 times faster than that of a single node with 9 computing nodes. It is proved that this framework has certain research significance in dealing with massive unstructured data. It not only provides theoretical reference value for the research of massive video processing but also makes a contribution to the actual industry.
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