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Due to the limitations of the learning environment and unguided guidance, students’ autonomous learning of foreign languages after class is not effective. In order to improve the efficiency of autonomous learning of foreign languages, this paper builds a foreign language self-learning system based on facial emotion recognition algorithm and cloud computing platform. Moreover, this paper uses emotion recognition algorithms to identify students’ status and guide them to improve students’ autonomous learning and improve the system’s operating efficiency through cloud computing platforms. In addition, this article combines the needs of autonomous learning to perform facial emotion matching and builds the corresponding functional modules of the system according to the requirements of autonomous learning and designs a sophisticated three-level network structure to achieve a balance between detection performance and real-time performance. In order to verify the performance of the system, an experiment was carried out through the accuracy rate of student’s autonomous state emotion recognition and the English improvement of students’ autonomous learning. The research results show that the foreign language autonomous learning system constructed in this paper has good performance.

Based on the former researches about transactive memory system and team performance, this article argues the relationship between team member, expertise, team stability, trust, communication and specialization, credibility, coordination of Transactive memory systems. Added team size as a control variable and task complexity as a moderator, we build a rudimentary model to show the mechanism that how TMS influences team performance, and hope it will help the following theoretical and empirical study.