Non-negative Matrix Tri-Factorization （NMTF） is an important part of the latent factor model. Because this algorithm decomposes the original data matrix into three mutually constrained latent factor matrices， it has been widely used in research fields such as recommender systems and transfer learning. However， there is no research work on the interpretability of non-negative matrix tri-factorization. From this view， by regarding the user comment text information as prior knowledge， Partially Explainable Non-negative Matrix Tri-Factorization （PE-NMTF） algorithm was designed based on prior knowledge. Firstly， sentiment analysis technology was used by to extract the emotional polarity preferences of user comment text information. Then， the objective function and updating formula in non-negative matrix tri-factorization algorithm were changed， embedding prior knowledge into the algorithm. Finally， a large number of experiments were carried out on the Yelp and Amazon datasets for the cold start task of the recommender system and the AwA and CUB datasets for the image zero-shot task to compare the proposed algorithm with the non-negative matrix factorization and the non-negative matrix three-factor decomposition algorithms. The experimental results show that the proposed algorithm performs well on RMSE （Root Mean Square Error）， NDCG （Normalized Discounted Cumulative Gain）， NMI （Normalized Mutual Information）， and ACC （ACCuracy）， and the feasibility and effectiveness of the non-negative matrix tri-factorization were verified by using prior knowledge.