This paper proposes a semantic recognition algorithm for legal article images based on deep learning, aiming to solve the recognition problems caused by complex backgrounds and diverse layouts in legal document images. By combining the ResNet-50 feature extraction module and the Bi-LSTM sequence modeling module, an end-to-end recognition framework is constructed, which can accurately extract semantic information from legal article images. The algorithm design covers three core links: data preprocessing, model optimization, and semantic decoding. The cross entropy loss function and CTC (Connectionist Temporal Classification) method are used to improve recognition accuracy and efficiency. Experimental results show that the algorithm is significantly better than traditional OCR and single CNN methods in accuracy, precision, and recall. After optimization, the accuracy rate reaches 94.2%, and the inference time is shortened by about 15%. By introducing the attention mechanism and parameter adjustment, the adaptability and computational efficiency of the model to complex scenarios are further enhanced. This study provides an efficient solution for the intelligent processing of legal documents and demonstrates the application potential of deep learning in the field of legal informatization.