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Machine translation is showing an increasingly broad application prospect as the Internet becomes more widely used. The method based on the principle of maximum entropy is primarily used in this paper to determine the subject clause for translation. Its feature set does not necessitate extensive linguistic knowledge, and it is less reliant on it than other methods. It can also add arbitrary features and flexibly combine a large amount of scattered and fragmented knowledge. As a result, the maximum entropy model is used as the classifier in this paper, and lexical and sentence features are used to effectively combine the rules and statistical knowledge. The subsentence recognition process is broken down into three stages, with different features extracted at different stages and the maximum entropy model applied multiple times. At each stage, the classifiers are trained. The translation accuracy and recall rate of Chinese gapped subject topic sentences are improved by more than 5% in a maximum entropy model, according to the results. This method of feature description is particularly useful for identifying sentence endings in Chinese subject topic sentences with gaps. Its translation is closer to the correct translation, indicating that the proposed method’s basic concept—the rationality of segmentation—is correct. The algorithm based on the maximum entropy model performs better in practice.

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