The invention provides an emotion tendency analysis method based on environmental element embedding and deep learning, which comprises the following steps: S1, collecting text data for training, and obtaining a word segmentation text; S2, training word vectors of the word segmentation text through Word2vec and Gloss, and then obtaining environment element embedding to serve as word vector representation of text semantics in a mode of expanding word vector characteristics of the word segmentation text; S3, automatically learning contexts to extract emotion comment objects by utilizing a neuralnetwork with BLSTM and a dynamic context window fused; S4, based on a local attention mechanism, training word vectors of the text semantics through BLSTM to obtain sentence-level feature vectors; S5, training sentence-level feature vectors through the convolutional neural network to obtain global text-level feature vectors; and S6, classifying the global text-level feature vector by using the multi-classification function Softmax to obtain the emotional tendency of the text data. According to the method, the text data sentiment tendency judgment accuracy is improved.