The intelligent algorithm model based on machine learning has become the most effective method at present to improve the automatic evaluation for the English translation of literary works. First, the translation rules and particularity of literary works are studied, and the index system of translation evaluation based on the variable features is established. Then, with the aid of the Python language platform, after the English translation is filtered and preprocessed by tools such as Stanford Parser and NLTK, the feature codes and feature degree are obtained with the Vector Space Model (VSM). Furthermore, the results are input into the Random-RF, Original-RF, and AHP-RF algorithm models for training and learning. Thus, the evaluation and analysis of translation quality are completed. The experimental results show that the AHP-RF model combining the analytic hierarchy process, the grey correlation method, and the random forest algorithm has better classification than the other two. Meanwhile, compared with the other four machine translation versions, the manual translation has a high quality score and a low classification error, and the corresponding evaluation results are consistent with the actual translation.