This paper investigates the application of deep learning (DL) in vocational education course evaluation and quality management, aiming to enhance the objectivity of evaluations and the efficiency of management. By collecting student learning data and teacher evaluation data, a DL model combining convolutional neural networks (CNN) and long short-term memory networks (LSTM), termed CNN-LSTM, was constructed for course evaluation. Experimental results demonstrate that the CNN-LSTM model significantly outperforms traditional methods in accuracy, recall rate, F1 score, and AUC-ROC, validating the effectiveness of DL in course evaluation and quality management. Despite the longer training time required for DL models, their exceptional classification performance justifies this investment. The study also discusses factors affecting the efficacy of DL applications, such as data quality, model complexity, and training strategies, and suggests that future research could focus on optimizing these aspects.