The invention discloses an electric power customer service work order sentiment quantitative analysis method based on a similarity word sequence matrix, and relates to an electric power customer service work order analysis method. A traditional sentiment analysis method cannot effectively discriminate sentiment intensity. The method comprises an emotion word expansion association step based on Word2Vec similarity, a multivariate emotion corpus construction step and a similarity word sequence matrix emotion quantization algorithm step, and is characterized by classifying and combing the work orders; cleaning the data, forming an initialized multivariate emotion word bank based on the Baidu word bank, carrying out work order text word segmentation by adopting a reverse maximum matching algorithm; based on the Word2Vec neural network, constructing the positive words, negative words, negative words, degree auxiliary words and the word vectors of the word sequences fused with customer appeal semantics, generating a learning model fusing appeal emotions through machine learning and training, and expanding a part-of-speech corpus based on a part-of-speech affinity-consensus relation, performing the emotion quantitative calculation by adopting a similarity part-of-speech matrix quantization algorithm, thereby completing the emotion quantitative analysis of a customer service work order, and effectively distinguishing emotion intensity differences.