Offensive speech on the internet seriously disrupts the normal network order and destroys the network environment for healthy communication. Existing detection technologies focus on the distinctive features in the text， and are difficult to discover more implicit attack methods. For the above problems， an offensive speech detection model BSWD （Bidirectional Encoder Representation from Transformers-based Sarcasm and Word Detection） incorporating irony mechanism was proposed. First， a model based on irony mechanism Sarcasm-BERT was proposed to detect semantic conflicts in speech. Secondly， a fine-grained word offensive feature extraction model WordsDetect was proposed to detect offensive words in speech. Finally， the model BSWD was obtained by fusing the above two models. The experimental results show that the accuracy， precision， recall， and F1 score indicators of the proposed model are generally improved by 2%， compared with the BERT（Bidirectional Encoder Representation from Transformers） and HateBERT methods. BSWD significantly improves the detection performance and can better detect implicit offensive speech. Compared with the SKS （Sentiment Knowledge Sharing） and BiCHAT （Bi-LSTM with deep CNN and Hierarchical ATtention） methods， BSWD has stronger generalization ability and robustness. The above results verify that BSWD can effectively detect the implicit offensive speech.