Text similarity measurement is the basis of natural language processing tasks, which play an important role in information retrieval, automatic question answering, machine translation, dialogue systems, and document matching. This paper systematically combs the research status of similarity measurement, analyzes the advantages and disadvantages of current methods, develops a more comprehensive classification description system of text similarity measurement algorithms, and summarizes the future development direction. With the aim of providing reference for related research and application, the text similarity measurement method is described by two aspects: text distance and text representation. The text distance can be divided into length distance, distribution distance, and semantic distance; text representation is divided into string-based, corpus-based, single-semantic text, multi-semantic text, and graph-structure-based representation. Finally, the development of text similarity is also summarized in the discussion section.

Chinese open-domain reading comprehension question answering is a task in the field of natural language processing. Traditional neural network-based methods lack interpretability in answer reasoning when addressing open-domain reading comprehension questions. This research is grounded in cognitive science’s dual-process theory, where System One performs question reading and System Two handles reasoning, resulting in a novel Chinese open-domain question-answering retrieval algorithm. The experiment employs the publicly available WebQA dataset and is compared against other reading comprehension methods, with the F1-score reaching 78.66%, confirming the effectiveness of the proposed approach. Therefore, adopting a reading comprehension question-answering model based on cognitive graphs can effectively address Chinese reading comprehension questions.