Vocational and technical education quality assessment based on deep neural network

In order to achieve evaluation accuracy and efficiency growth, this paper develops a method based on deep neural network (DNN) to realize the automated evaluation of vocational and technical education quality. The deep neural network model is used to train and predict multi-dimensional data such as student scores, teaching feedback, and teacher qualifications. The K-fold cross-validation technology is used to ensure the stability of the model and the generalization ability. The experimental results show that the DNN-based model shows excellent performance in terms of mean square error (MSE) and accuracy compared with the traditional evaluation method. The MSE result of the experimental group is 0.02, and the accuracy rate reaches 85%, while the MSE of 0.05 is the value of the control group, and the accuracy rate is 78%. In terms of improving evaluation accuracy, reducing manual intervention, and optimizing educational decision-making, deep neural networks have outstanding advantages. The conclusion shows that the evaluation method based on deep neural networks provides effective technical support for the intelligent evaluation of vocational and technical education quality, and has a wide range of application value.