The quality of college graduates is directly related to the social reputation and development of colleges and universities. In order to accurately evaluate the quality of college graduates, based on the historical data of computer graduates in a university, this study uses an improved random forest algorithm to build a talent training quality assessment model. Before training classifiers, RF ranking method is used to measure the importance of features and select 75% of the features for dimension reduction, so as to improve the unbalanced phenomenon of training samples; through the training of base classifiers, the performance of each classifier is tested, and a single classifier is weighted according to the strength of performance, so as to reduce the impact of poor performance classifiers on the results. The practical results show that the algorithm improves the accuracy and precision of the quality assessment of personnel training, and can play a guiding role in personnel training in colleges and universities.