In this paper, an English teaching cultivation evaluation model based on RBF neural network is designed to solve the problems of poor generalization ability, low evaluation accuracy, and correlation coefficient of the current teaching evaluation model. Before establishing the evaluation model, conditional information entropy is used to screen the evaluation indexes of the English teaching cultivation function, and six dimensions of core literacy and five dimensions of thinking ability are selected as evaluation indexes. A new relative merit matrix is constructed under the constraint of relative merit by using the screened conditional attributes. Selection, crossover, and mutation operations are used in the RBF neural network to select the optimal parameters through the introduction of the genetic algorithm. Finally, the assessment data of students after project-based English teaching are analyzed for values and nurturing functions using the evaluation model. According to the findings, project-based English teaching led to the highest average score of 29.15 for students’ values compared to the dimensions of interest, ability, and personality. In terms of the six dimensions of core literacy and the five dimensions of thinking ability, the score difference between the experimental class and the control class is more than 1.2 points, and the Sig value of each dimension is less than 0.05, which indicates that English teaching based on Project-Based Learning promotes students’ values, core literacy and thinking ability.