To identify relevant extreme speech， a gender opposition speech recognition method of fusing multi-features and emoji sentiment lexicon is proposed. Firstly， BERT（Bidirectional encoder representation from transformer） is used to extract the character features of the input texts， and Word2Vec is used to extract the Wubi， Zhengma and Pinyin features of the input texts. Then， these features are fused and fed into the Bi-GRU（Bi-directional gated recurrent unit） network to obtain the deeper semantic information. Finally， the sentiment polarities are calculated with the full-connected layer and SoftMax function combining the emoji sentiment lexicon to determine whether the input texts are related gender opposition. Compared with the method without adding multi-features and emoji sentiment lexicon， the experiments on the self-collected Chinese gender opposition dataset show that the proposed model is improved on the F₁ value by 5.19%. In addition， the generalization of the proposed method is verified by experiments on the public Chinese sentiment analysis dataset Weibo_senti_100k.