Character Representation and Application Analysis of English Language and Literature Based on Neural Network

Abstract

The development of computer technology has promoted the continuous progress of Natural language processing technology and the great development of ideology and culture, and also prompted literary workers to create a large number of literary works. This poses a new challenge to the application of Natural language processing technology. Text analysis and processing is realized by Natural language processing technology. In the information society, the amount of data is increasing exponentially, and the number of literary works produced is also rapidly increasing. In order to gain a comprehensive understanding of domestic and foreign history and culture, some Chinese readers are not only satisfied with reading Chinese works from ancient and modern times, but also hope to read and understand foreign literary works. Current mainstream methods for literary character analysis are manual, making the results highly subjective and inefficient for large-scale literary works. To address this problem, this study proposes a character representation and analysis method based on neural networks using English novels as an example. By preprocessing data and utilizing the word dependency relationship to represent character vectors and calculate similarity, the study uses the Skip-gram model to train character vectors and K-means for clustering. An AGA-BPNN model is proposed for character and gender prediction and classification, with a 95.42% accuracy rate achieved in character prediction classification, and an average accuracy, recall, and F1 score of 0.953, 0.962, and 0.962, respectively, in gender prediction and classification. The results demonstrate the effectiveness of the method and propose a new approach for novel character analysis.