Emotion is the most important semantic information of music. Music emotional classification is widely used in music retrieval, music recommendation, and music therapy. Traditional music emotional classification is mostly based on audio. Nevertheless, based on the current technology level, it is difficult to extract semantic-related audio features from audio. There is some emotional information in the Lyric text, and music emotional classification is carried out with lyrics. This study focuses on Chinese lyrics and constructs a reasonable music emotion dictionary, which is the premise and foundation of lyric emotion analysis. Therefore, a Chinese emotion dictionary in music field is constructed based on Word2Vec, and Chinese music emotion analysis is carried out based on the weighting of emotional words and the part of speech. Firstly, this study constructs the emotional lyrics table based on the VA emotional model and adopts Word2Vec. The idea of word similarity calculation in Word2Vec extends the emotional vocabulary and constructs a Chinese music emotional dictionary, which contains the emotional categories and emotional weights of each word. Then, according to the dictionary, emotional words weights are obtained, and feature vectors of lyric texts based on TF-IDF (Term Frequency-Inverse Document Frequency) and lexical features are constructed. Finally, music emotional classification is realized. The constructed music emotion dictionary is more suitable for music field, and the accuracy can be improved by considering the influence of part of speech when constructing feature vectors.