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Focused on the problems of large feature dimension and low recognition rate in current emotional recognition research, an emotional recognition method based on feature fusion of multiple physiological signals (ECG, EMG, RSP, SC) and FCA-ReliefF algorithm is proposed. The features extracted from time domain and frequency domain are fused as the input of the classifier. In order to reduce feature dimension, feature correlation analysis (FCA) was used to eliminate features with strong correlation. Then ReliefF was used to delete features with weak classification contribution. Experimental analysis on the public dataset and compared with related studies show that the proposed FCA-ReliefF dimensionality reduction strategy can effectively reduce the feature dimension from 108 to 60, and the emotion recognition rate is improved up to 98.40%, which is better than the reported experimental results from the perspective of feature dimension and recognition accuracy.