  Researching the specificity of TCR contributes to the development of
immunotherapy and provides new opportunities and strategies for personalized
cancer immunotherapy. Therefore, we established a TCR generative specificity
detection framework consisting of an antigen selector and a TCR classifier
based on the Random Forest algorithm, aiming to efficiently screen out TCRs and
target antigens and achieve TCR specificity prediction. Furthermore, we used
the k-fold validation method to compare the performance of our model with
ordinary deep learning methods. The result proves that adding a classifier to
the model based on the random forest algorithm is very effective, and our model
generally outperforms ordinary deep learning methods. Moreover, we put forward
feasible optimization suggestions for the shortcomings and challenges of our
model found during model implementation.
