In order to mitigate the security risks and economic losses caused by electricity theft, and to efficiently and accurately identify theft users while discriminating their theft patterns, propose an electricity theft detection and type discrimination method that integrates a convolutional neural network(CNN) with a bidirectional long short-term memory(BiLSTM) network. Initially, an open energy data initiative(OEDI) dataset comprising 16 types of electricity users is employed. The dataset is modified according to six distinct theft patterns and subjected to Min-Max normalization to eliminate the influence of differing feature scales. Subsequently, the model extracts multi-scale local features via convolutional layers and further expands the receptive field using dilated convolution, thereby effectively capturing subtle variations amid environmental interference. Thereafter, BiLSTM is utilized to model the sequential data in both forward and backward directions, comprehensively capturing long-range dependencies and contextual information. To enhance the model′s robustness and generalization capability, dropout and dynamic learning rate adjustment mechanisms are incorporated. Finally, experiments are conducted under binary, six-class, and seven-class classification tasks with varying training set ratios. The experimental results demonstrate that the proposed method significantly outperforms traditional approaches in terms of accuracy, AUC, and F1-score, thereby validating its effectiveness in electricity theft detection and type discrimination under complex scenarios.