The literature uses a method that combines word position and word span to improve TFIDF weight, or uses word frequency and word position features to perform weighted analysis by using semantic coherence. The literature proposed a text key phrase extraction method based on LDA and TextRank. In order to further improve the effect and quality of keyword extraction, many scholars have improved the above algorithm. Therefore, the relationship between characters, such as frequency of occurrence, and the combination probability reflect whether they become a word. If a few consecutive words appear more often in the same statistical time period, they are likely to be a word. It judges whether a word is a keyword based on the statistical frequency of a word. Keyword extraction algorithm based on statistics refers to an algorithm that uses the frequency of statistical words to perform related processing. Therefore, more manpower and material resources are needed to improve this work. This makes the processing of English word segmentation more complicated. However, English words are ever-changing, and different combinations of words and words have different meanings. At present, we still use English word segmentation technology for pre-processing. English keywords are automatically extracted, and the process of English word segmentation cannot be avoided. However, these methods are based on mechanical participle and comprehensive participle as the basic principles, and the final processing results are also different.

Currently, there are many word segmentation methods, and word segmentation algorithms are also different. Word segmentation processing in these languages is also the key to intelligent text information technology. At the same time, texts in oriental traditional languages such as Japanese and Korean have the same characteristics. If we want to get a single word, we need to perform English word segmentation first. However, only by forming words with letters can we express specific meanings. The characteristic of English is that there is no obvious word boundary, and each word can appear independently. When we use a computer to extract keywords from a specific document, we can clearly understand the theme and main content of this document in a short time, so that we can obtain valuable information and improve our overall grasp of the content of this document. The main task of automatic keyword extraction is to extract words that can express the main idea, main content, and author’s opinion of the article from a specific article. The network nodes are output in descending order of comprehensive feature values, single word nodes are removed, and the first K words are extracted as text keywords. Moreover, this paper constructs the comprehensive eigenvalue formula of nodes based on the degree centrality, eigenvector centrality, and node betweenness centrality of network nodes. Based on complex networks, the algorithm uses natural sentences as window units, words as nodes, co-occurrence relationship between words as edges, and word co-occurrence degree C (wxwy) based on the co-occurrence frequency as the weight of the edges to construct a text word co-occurrence network model based on a complex network. This paper takes a single document as the research object and proposes an algorithm for extracting keywords from English text based on a complex network. Keyword extraction technology is a key technology in the field of text information mining.

The research shows that the English keyword information extraction model constructed in this paper is effective. After the model is constructed, the performance of the model is verified through experiments.

In addition, the algorithm solves the local parameterization problem of partially deleted vertices after the simplification operation of each layer is completed. Moreover, based on the proposed normal vector-based QEM mesh simplification algorithm, the point selection after the edge folding operation is fixed as the vertices of the original edge, and it is applied to the mesh parameterization. In order to improve the extraction efficiency of English keyword information, based on the CAD mesh model, this paper adds constraint factors such as vertex neighborhood flatness, vertex degree, side length, and flatness on both sides of the side on the basis of the original QEM quadratic error simplification algorithm, and it incorporates a smoothing effect into the edge folding cost function. Traditional methods only consider topic information in English vocabulary information extraction, lose the statistical feature information of the keywords themselves, and easily ignore the semantic information of the words.

Arbitrary coherence