Emotion plays an indispensable role in human behavior and cognition. It is of great practical significance to carry out research on emotion recognition. To improve the accuracy of cross-subject identification of four kinds of emotional states, the feature representation method based on the full-view and ELM-Adaboost method is proposed. Firstly, a data processing strategy based on the fused information is proposed. The sample data consisted of multiple types of physiological signals are cross-fused to help extract sample features from the perspective of full view. Secondly, the feature selection method with the maximum correlation and the minimum redundancy is used to select the fused features to achieve effective emotional state representation. Finally, the strong classifier constructed by the ELM-Adaboost method is employed to realize emotion recognition. Experimental results from multi-perspective evaluate the effectiveness of the proposed method. The identification accuracy of cross-subject with four types of emotional states reaches 83. 06% .