Research on Undergraduate Talent Training Model for Tourism Management Major Based on Fuzzy Evaluation Algorithm and Neural Network Architecture

At present, China's tourism industry is in an important stage of transformation and upgrading. The structural contradiction between the supply and demand of tourism talents has become increasingly apparent. High end tourism talents with interdisciplinary abilities have become an important support for the development of the industry, but the shortage of talents has also become a bottleneck restricting the further development of the industry. In this context, the practical teaching mode of undergraduate tourism management major, as a key link in cultivating composite senior talents, is of great significance for ensuring the quality of talents. This article combines neural network architecture to design a new higher education quality evaluation model and proposes an innovative fuzzy evaluation algorithm, providing a new technological path for the implementation of educational informatization. Specifically, this article first analyzes the relationship between key elements such as course arrangement, training objectives, and graduation requirements in the talent cultivation system and the nodes at various levels of neural networks. Through a neural network architecture, the hierarchical relationship and operating mechanism between these elements are described, and the OBE (Outcome Based Education) concept is introduced, providing a theoretical framework for constructing an education quality evaluation model. Furthermore, this article proposes a fuzzy evaluation algorithm that combines a class neural network architecture, which can comprehensively integrate forward propagation, multi-level feedback, and fuzzy quality assessment mechanisms, providing a theoretical basis for identifying weak links in the process of student ability development. This algorithm provides decision support for the continuous optimization of the talent cultivation system by combining quantitative analysis of process data and forward propagation mechanism with comprehensive evaluation of multi-party feedback.