In order to solve the problem of monitoring and analyzing massive traffic video data, the in-depth research on traffic video surveillance technology in the context of hadoop big data is conducted, and a design scheme of anomaly jam detection algorithm is proposed based on traffic video data to realize traffic real-time data update and anomaly analysis. At the same time, for the massive traffic monitoring video, a parallel implementation algorithm is designed based on Hadoop component MapReduce. Finally, the effectiveness and accuracy of the algorithm is verified by actual traffic data of a city in Zhejiang Province. The algorithm in this paper can effectively calculate the traffic congestion and abnormal conditions. Compared with the traditional scheme, this scheme can focus on the time granularity in the range of 10 min to analyze the traffic situation in real time. Compared with the traditional distributed computing model, the 10 minute delay of this scheme can be controlled at 2.1 s, which is 81% lower than the traditional scheme, which basically meets the real-time, fine-grained requirements for traffic video surveillance requirements.