Aiming at the problem of detecting illegal gambling websites in massive websites， this paper proposes a classification method based on BERT-BiLSTM and multi-classifier decision-level fusion. This method improves the classification performance by adopting the following steps. Firstly， it extracts the textual information considered with high priority， i.e.， meta information in HTML head and hyperlink titles on a web page， to enhance the richness of textual features. Secondly， a novel text classification model based on BERT-BiLSTM is designed， and it is proved superior in learning better sentence feature representatives and boosting performance. At last， the decision-level fusion is performed on the classification results from multiple dimensions （i.e.， website title， keywords， and page text） to further improve the performance and robustness of the entire system. Moreover， a variety of strategies generating suspicious domain names are used to improve the ability to actively detect illegal websites. Experimental results and running results in real cyberspace demonstrate the effectiveness of the proposed method.