目前，音乐歌词情感分类大多以二标签极性情感为主，多情感标签分类较少，并且对于情感性不确定的歌词而言，得到的分类性能不高。为了解决多情感标签研究分类的不足，以及提高分类准确性，提出一种利用Word2Vec词嵌入技术，并使用多核卷积神经网络作为分类器的音乐歌词多情感分类方法。该方法首先结合音乐歌词文本，进行数据预处理和可视化分析；其次利用Word2Vec词嵌入提取歌词局部特征，构建特征情感向量，挖掘歌词中情感信息，将歌词转化为更利于分类器模型输入的词向量；最后在分类器中，选用卷积神经网络模型，并在此基础上采用不同高度卷积核的方式构建新模型以此得到多情感分类。结果表明：音乐歌词多情感分类的结果达到94.26%，与传统CNN相比，分类精确率提高了6.86%，取得了良好性能。

Currently, most of the music lyrics emotion classification is based on two-label polar emotions, while multi-emotion label classification is rare, and the classification performance obtained is not high for lyrics with uncertain emotionality. To address the limitations of multi-sentiment labeling research in classification and to enhance classification accuracy, a multi-sentiment classification method was proposed for music lyrics using Word2Vec word embedding technology and a multi-core convolutional neural network as the classifier. The method initially integrated music lyrics text for data preprocessing and visualization analysis. Secondly, Word2Vec word embedding was utilized to extract local features of the lyrics, construct feature sentiment vectors, mine sentiment information within the lyrics, and converted the lyrics into word vectors that were more suitable for input into the classifier model. Finally, a convolutional neural network model was selected as the classifier, and upon this foundation, a novel model was constructed with various heights of convolutional kernels to achieve multi-emotion classification. The experimental results show that the result of music lyrics multi-sentiment classification reaches 94.26%, which improves the classification accuracy by 6.86% compared with the traditional CNN and achieves good performance.