URL detection is an important task in the field of Internet security. To address the problems of low model accuracy and weak generalization ability to exist machine-learning-based malicious URL detection methods, a URL classification method based on a combination of the BERT model and the convolutional neural network (CNN) is proposed. First, a regular expression-based approach is used to achieve pre-processing of URL data; second, a malicious URL classification method combining BERT pre-training model and CNN is designed to effectively handle contextual relevance and local features in malicious URL detection tasks; finally, experiments are conducted on a dataset of more than 30,000 URLs to evaluate its performance and efficiency. The experimental results show that the Bert-CNN model outperforms other deep-learning-based and traditional methods in terms of accuracy, and our model achieves a 99.5% accuracy on the test dataset.