RETRACTED ARTICLE: Coastal soil pollution detection and business English teaching index construction based on deep feature fusion

Abstract

This paper proposes a deep learning model, which classifies web pages based on deep feature fusion. The model has the function of using Text CNN to extract the text of important tags in the web page. It also has the function of using XL Net to extract the text of other tags in the web page. The two parts of the function in the webpage are combined together, which effectively solves the problem of feature collinearity and vector sparseness in the process of network deep feature fusion. This article discusses a deep merging mechanism that can improve the merging of the semantic features of important tags with the semantic features of other web pages. Experimental results show that this method is based on a deep fusion deep learning model and can be used for efficient and high-precision classification of website text. At the same time, the development index of an effective English language model for business research and index system is established in the study. The research is based on theoretical analysis, concept deduction, teaching reform projects, textbook drafts, teaching materials, teacher training basic knowledge, and teaching competitions. Theoretical model of English teaching development index. In addition, this article also introduces the actual soil of a certain area of China as the research object, and 16 kinds of phthalate esters were tested in the soil of this area. By determining the physical and chemical properties of soil enzymes, the effects of phthalate microbial communities, and soil microbial communities on different planting methods were analyzed. This paper uses deep functional fusion technology to detect soil pollution and display the concentration of phthalate pollutants in the soil, as well as the response of business English to the construction of teaching index.