Design and implementation of information system for vocational education quality assessment based on CNN-BiGRU

With the development of information technology, the informatization of vocational education quality assessment has become an essential means to improve education quality. This study aims to design and implement an information system for vocational education quality assessment based on CNN-BiGRU (Convolutional Neural Network-Bidirectional Gated Recurrent Unit) to solve the problems of intense subjectivity and low efficiency in traditional assessment methods. Firstly, the present situation of vocational education quality evaluation and the necessity of informatization construction are analyzed, and the objectives and requirements of system design are defined. Then, the system architecture design is expounded in detail, including the data acquisition module, feature extraction module, evaluation model construction module and result display module. In feature extraction module, CNN is used to extract deep features of vocational education-related data. In the evaluation model building block, BiGRU is used to model feature sequences to capture long-term dependencies in time series data. Experimental results indicate that the designed system can improve accuracy and efficiency of evaluation. Specifically, compared with traditional evaluation methods, the evaluation accuracy of this system is enhanced by 15 %, and the evaluation time is shortened by 30 %. In addition, the system shows good stability and scalability in practical application, which provides strong support for informatization of vocational education quality evaluation. This study enriches theoretical system of vocational education quality evaluation and provides a feasible technical scheme for practical application, which has important theoretical and practical significance.