The generation of Surface Electromyography is ahead of the occurrence of body movement and has the ability to predict body movement, which often assists patients in rehabilitation training. To solve the problem that single channel sEMG signal is difficult to predict people' joint angles effectively, this paper proposed a maximum Relevance minimum Redundancy based on multi-channel EMG feature acquisition and Particle Swarm Optimization feature selection algorithm. The performance of mRMR-PSO algorithm was verified by comparing with that of mRMR algorithm and Principal Component Analysis algorithm for joint Angle prediction accuracy. Experimental results show that the joint angle prediction accuracy of mRMR-PSO based on multi-channel feature selection algorithm is 32.6% and 14.9% higher than that of mRMR and PCA, respectively, which verifies the effectiveness of the algorithm,and the algorithm is applied to actual scenarios.