Research on intelligent evaluation model of vocational education curriculum based on diffusion reinforcement learning and multi-scale residual network

With the rapid development of vocational education informatization, how to accurately evaluate curriculum quality and learning effect has become an important topic in the field of education. This paper proposes an intelligent evaluation model based on the combination of Diffusion reinforcement learning and multi-scale residual network, aiming at improving the evaluation accuracy and intelligence level of vocational education courses. The model is designed to scale efficiently for large datasets and diverse educational platforms, ensuring its applicability across different regions with varying curricula and student engagement. By designing a comprehensive model, this model can monitor the learning process in real time, analyze students’ learning behaviors, and ensure the security of data based on network security requirements. Based on the Diffusion reinforcement learning method, this paper uses the state, action, and reward mechanism in the reinforcement learning framework to dynamically evaluate the course content. The training process of reinforcement learning obtains real-time feedback through interaction with students, forming an accurate curriculum evaluation model. Experimental data show that the model based on Diffusion reinforcement learning improves the accuracy of course evaluation by about 15%, and the evaluation error of the model is reduced by 20% compared with traditional evaluation methods. Combined with multi-scale residual network, the curriculum data is deeply analyzed, and the accuracy and robustness of the evaluation system are enhanced by multi-level feature extraction. The introduction of MSRN enables the model to extract features at different scales, further improving the depth and accuracy of understanding of course content. The experimental verification shows that the evaluation accuracy of the model after using MSRN is improved by 18%, and it performs well in the adaptability of different course contents, especially in complex data sets. Considering the network security, this paper specially designs the data transmission and storage mode based on encryption technology to ensure the privacy and security of students’ data. By adopting SSL encryption protocol and firewall technology, the system can effectively resist external attacks and ensure the integrity of evaluation data. Experiments show that under the premise of network security, the evaluation results of the model are not affected by malicious attacks, and the stability of the model is improved by 25%. The intelligent evaluation model proposed in this research has significant advantages in improving the accuracy and robustness of vocational education curriculum evaluation, especially in the context of complex data and network security, and shows a good practical application prospect.