Identification of network negative news has important research significance in network public opinion monitoring. Aiming at the problem that negative news is difficult to detect under the current mass data

this paper proposes a method of negative news recognition based on emotional computing and hierarchical multi-head attention mechanism. Firstly

this paper uses TFIDF (Term Frequeney-Inverse Document Frquency) and emotional similarity algorithm to construct negative news emotional lexicon from news texts. Secondly

this paper uses the method of emotional tendency calculation to calculate the degree of emotional tendency of negative news affective words. Finally

the model vectorizes the emotional tendencies of words and expressions

and use hierarchical multi-attention model to judge the positive and negative emotions of news. The introduction of emotional computing and multi-attention mechanism is of great help in capturing emotional words in texts. Finally

this paper compares the real network news text data with many existing algorithms

and proves that the model has a good recognition effect. Compared with the Han model and LSTM model

it is increased by 0.67% and 3.29% respectively.