# Authorship Detection on Classical Chinese Text Using Deep Learning
## 1 Introduction  
Authorship detection, a subset of authorship analysis within social information science, is a classification problem [1], whose aim is to discover the particular author of a text from a set of candidates. The authorship analysis of handwritten texts has been well-researched since ancient times, as every author has a unique writing style. In authorship detection an author is identified by analyzing the writing style characteristics based on stylometry [2]. It is challenging to extract from texts the most distinctive characteristics that reflect the author's writing style. The accuracy of detection depends on the relevance of the extracted features. Lexical [3], syntactic [4], and linguistic features [5] are the most distinctive characteristics of an author's writing style. Nowadays, large numbers of textual documents have been digitized and made available on the Internet [6]. Authorship detection has advanced alongside the development of science and technology. Several methods have been applied to authorship detection. Keystroke biometrics [7] is based on using software applications; it can extract featuresfrom the rhythm when an author is typing words. The most common and popular method is stylometry-based [8], which uses data analysis techniques to extract the most distinctive features of an author's writing style. It has also been suggested that sentimental words significantly affect authorial writing [9]. Text mining makes a great contribution to authorship detection, as it is widely utilized to extract meaningful features from large quantities of data in various unstructured formats. Text dataset pre-processing tends to be the starting point [10]. The selected characteristics are employed to convert the text into a feature set. Machine learning is an important tool in data mining [11], utilized to identify patterns from data and create analysis models to extract meaningful information. A feed-forward neural network language model can be applied for authorship detection [12]. The author's set size is decent and the complexity is significantly reduced while improving the accuracy of author detection. When a multi-headed recurrent neural network language model is used [13], it can reach high accuracy even with small dataset. _Dream of the Red Chamber_ (DRC), also called _The Story of the Stone_, written by [PERSON] during the Qing Dynasty in the 18 th century, is known as the pinnacle of the Four Great Classical Novels of Chinese literature and a gem of world literature [14]. DRC, with its unique artistic style, profound ideological content, superb narrative skills and rich cultural connotations, has occupied a pivotal position in the history of world literature. Through its profound analysis of feudal society and deep concern for human nature, it demonstrates the common problems of human society and the universal characteristics of human nature, providing valuable ideological resources and spiritual wealth for world literature. The novel is about a large feudal family with declining fortunes. It is a fictional reflection of [PERSON]'s own family. Considered the encyclopedia of Chinese feudal society, it has aroused extensive attention from researchers on authorship attribution in classical literature [15]. The authorship detection of the first 80 and the remaining 40 chapters of the novel DRC is a well-known unsolved mystery about authorship attribution in classical Chinese literature [16]. [PERSON] did not finish his novel when he died in 1763, leaving about 80 chapters in circulation. [PERSON] and [PERSON] claimed that they had collected more chapters of [PERSON] through different channels. They put them together and published the first printed version. However, the authorship attribution of the last 40 chapters caused widespread controversy. The renowned critic [PERSON] considered them forgeries written by [PERSON] [17], while some scholars believed that [PERSON] made a mistake and took someone else's forgery as the original.