Construction of an International Equipment Manufacturing Industry–Education Integration Innovation Quality Prediction Model Based on Deep Learning Algorithms

To solve the problems of low accuracy and low efficiency in predicting the quality of international equipment manufacturing industry–education integration innovation, a deep learning algorithm is used to design a method for predicting the quality of international equipment manufacturing industry–education integration innovation. First, data collection and preprocessing are carried out, including collecting relevant historical data and normalizing it. Then, the partial least squares method is used to reduce the dimensionality of the data and extract the main features. Then, build a long short-term memory neural network model for learning the temporal dependencies in historical data and predicting the future innovation quality. Finally, the sparrow search algorithm is used to optimize the hyperparameters of the model and improve its predictive performance. Through experiments, it has been proven that the method proposed in this paper for predicting the quality of international equipment manufacturing industry–education integration innovation maintains a recall rate of over 93%, with a logarithmic loss of 0.05 and a root mean square error of 0.1, which is much lower than the two compared methods. This method provides a more accurate prediction of the quality of industry–education integration innovation and improves the efficiency of education and industry integration.