The invention provides a dynamic self-adaptive multi-
modal sentiment analysis fusion method and
system, and relates to the technical field of multi-
modal sentiment analysis. The method comprises the following steps: synchronously acquiring voice, text,
facial expression and limb movement data of a target user to form a multi-
modal data set; the method comprises the following steps: firstly, extracting emotional characteristics of each mode, and constructing a cross-mode correlation model to capture a collaborative and complementary relationship among different
modes; and calculating a real-time
confidence score and a complementarity index of each modal based on the weight matrix of the cross-modal correlation model. Then, according to the scores and the indexes, a weighted average or maximum entropy
algorithm is dynamically selected to fuse multi-modal emotion features, and a comprehensive emotion
feature vector is generated; and finally, inputting the vector into a pre-training
deep learning model, and outputting an emotional state category of the user. According to the method, the user emotion can be accurately and comprehensively captured, efficient
emotion recognition and classification are realized, and the robustness and flexibility of an emotion analysis
system in a complex scene are improved.