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In order to realize the automatic control of stage lighting of music emotion recognition, it is necessary to mark the emotion of music file. Aiming at the problem of low efficiency and slow speed of artificial emotion marking, the stage lighting control method based on music emotion recognition is studied, and a music emotion feature extraction, classification and recognition algorithm based on support vector machine and particle swarm optimization is proposed.Taking 231 MIDI music files as an example, the basic features of music, such as average pitch, average intensity and melody direction, are extracted and standardized. Then and then the multi-classifier of support vector machine (SVM) is formed, and the parameters of classifier are optimized by using improved particle swarm optimization (PSO) algorithm to establish standard music classification model. Finally, the lighting is designed action model, the new music file is matched with the lighting action through the discrete emotion model, and the stage lighting control method is generated. Experimental results show the effectiveness of the emotion recognition model, compared with SVM traditional multi-classification model, obviously improve the recognition rate of music emotion, reduce the test time, so as to provide a reasonable reference for stage lighting designers.