Error recognition of english translation text based on neural network and fuzzy decision tree

In response to the low accuracy and recall of current English translation text error recognition methods, this paper proposes a research on English translation text error recognition based on an improved decision tree algorithm. Firstly, use mutual information to calculate the degree of relevance of entries and annotate the part of speech of English translation texts. Then, by using an encoder and decoder to construct a neural network structure, the neural network is applied to the process of feature extraction in machine English translation, and the softmax function is used for normalization. Finally, a fuzzy decision tree is used to segment each information feature, and combined with the Gini index, error features are classified to achieve English translation text error recognition. Through experiments, it has been proven that the accuracy of the translation text error recognition method proposed in this article remains above 94%, the recall rate remains above 86%, the recognition accuracy is high, and the judgment is reliable. The improved decision tree algorithm exhibits consistent performance across varying data volumes, achieving an accuracy exceeding 93% when handling 15,000 to 50,000 sentences, surpassing comparable state-of-the-art algorithms. As such, the enhanced approach for identifying errors in English translation texts effectively enhances translation quality and demonstrates promising potential for practical applications.