The words of National Social Science Foundation (NSSF) titles are expressed into the train and test corpus. And then, the category determination model of the NSSF project by using the conditional random field model and the bidirectional short and long time memory model is verified from many angles and levels. The results are compared with the experimental results of the support vector machine model. Based on the corresponding model performance evaluation indexes, this paper not only verifies the overall performance of the traditional machine learning model on the small-scale corpus, but also proves that the overall performance of the conditional random field model with the artificial feature model is not certain to be outstanding, meanwhile, the performance of the conditional random field model is analyzed in a case.