Based on AHP Evaluation Model and Neural Network Backbone to Achieve Specialized and Integrated Talent Training

Based on the talent training mode that combines innovation and entrepreneurship with professional education in applied undergraduate colleges, this paper provides a talent training mode with three complete education, three spiral theory and three methods of scientific evaluation system for cultivating high-quality talents with innovative thinking and practical ability. Among them, the analytic hierarchy process (AHP) evaluation model is adopted, determine the weight value of elements in the talent training mode, and pay more attention to the important elements. Based on talent training mode, CNN-MHSA-LSTM model is constructed by using convolutional neural network, attention mechanism and recurrent neural network to predict students' academic performance in professional courses and public courses. To evaluate the students' process performance and predict whether they can pass the final grade. By comparing the courses of different nature, the prediction accuracy is above 86<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>, which can realize the grade warning better.