Strategic Design of Artificial Intelligence-Assisted Professional Literacy Teaching Content in the Modern Service Industry Program in Higher Education Institutions

Abstract

The modern service industry is a new engine driving economic and social development, and the introduction of intelligent technology to optimize the optimization of professional teaching content can provide a source of motivation for the cultivation of high-quality professional service talents. This paper selects two dimensions of teaching resources recommendation and professional setting of the modern service industry in higher vocational colleges for analysis. In terms of teaching resource recommendation, the driver algorithm is used to collect teaching resources of the modern service industry, extract the resource characteristics of teaching content through keywords, and input them into the intelligent recommendation model of teaching content that combines capsule network and attention mechanism. In terms of adaptability of professional settings and industrial structures, the adaptability of modern service industry teaching can be verified through the aggregation degree, coincidence degree, and deviation degree. In this way, the optimization strategy for teaching content and professional settings in the modern service industry in higher vocational colleges is proposed. When using the model of this paper to carry out the recommendation of teaching resources for the modern service industry, the maximum gap in the COV value of the course teaching resources recommendation is 6.4 percentage points, and the fluctuation range of the NDCG value is between 4.45 and 4.79. The fit between the structure of students enrolled in the modern service industry and the tertiary industry ranged from −0.352 to −0.396, and the mean values of industry deviation and employment deviation were 0.292 and 0.499, respectively. Higher vocational colleges and universities should continuously promote the fitness of teaching resources and students, and establish a benign closed-loop consensus mechanism for industrial development and specialty settings.