Aiming at the scientific, accurate, and operable regional independent innovation capability evaluation classification, a Decision Tree Genetic Algorithm and Back Propagation neural network (DTGA-BP) is proposed. The characteristics of the evaluation index are selected and the structure of the neural network is improved by optimizing the number of neurons in the hidden layer. The genetic operation of the nonlinear crossover probability value is combined with a new selection operator to optimize the initial weight and threshold of the BP neural network. The experimental results show that the evaluation results of the combined model are more scientific and accurate than the traditional subjective valuation method. Compared with the single BP neural network model and the GA-BP model, the classification accuracy is improved by 41% and 20%, respectively.