The rapid development of mobile internet has brought new opportunities to the development of the animation industry, but the network traffic bottlenecks animation video promotion. Based on the super-computing resources, using the Python language, adopting FFMpeg, OpenCV, HEVC and PyGTK technologies, we designed and implemented an animation video resource conversion system to solve the traffic bottleneck in this paper. The conversion system provides HEVC parallel conversion, video serial conversion, video frames extraction and video playback functions, effectively promoted the promotion of animation products distribution.

The full text retrieval system can receive constant feedback in the form of user behavior. In the case of a search engine, each user will immediately provide information about how much he likes the results for a given search by clicking on one result and choosing not to click on the others. This paper will look at a way to record when a user clicks on a result after a query, and design a Click-Tracking Network. Then training it with BP neural networks to intelligently improve the rankings of the results for users. Finally, we implement a search and ranking system content-based ranking and improve the search and ranking with neural network. By experiments we have shown good results.