Multimodal discourse analysis of automotive websites

Abstract

Abstract In this paper, we study the multimodal discourse of automotive websites, aiming to contribute to the improvement and upgrading of automotive websites. To this end, this paper mines the data of advertising discourse, website visual effect, and feedback of auto website usage based on the KNN algorithm optimized by K-value selection strategy and analyzes the language effect of advertising discourse, rhetorical techniques, website page design, feedback of usage, and website advertising visual effect of the auto website with the help of multimodal discourse analysis framework. In terms of language, most of the page language and advertising language are concerned with syllabic symmetry, harmonization, and rhyming and focus on rhetorical techniques, such as 21% of the ads use similes and 14% use puns. In terms of content, more than 80% of people were satisfied with the design of the website and felt that the design of the website pages was reasonable, simple, and generous, and the ratio of positive to negative markers such as “satisfaction points” and “car quality evaluation” was close to 7:3, indicating a good recognition. The ratio of positive to negative marks for “satisfaction points” and “car quality evaluation” is close to 7:3, indicating good recognition. In terms of visual design, more than 80% of people think that video ads on auto websites are very effective and can arouse audiences’ interest and make them participate in them actively. Although the overall satisfaction of Chinese auto websites is good, there are many areas for improvement, and these aspects need to be optimized to present better results.