Application and optimization of BP prediction model driven by internet of things in tourism education

This paper investigates the participation of industry-school cooperation and talent cultivation models in tourism schools within the framework of the internet of things (IoT) by employing a back propagation (BP) prediction model. The paper delves into the participation model of industry-school cooperation under the IoT context, elucidating the application and optimization of the BP prediction model in the specific environment of tourism education. Experimental results validate the proposed model's effectiveness, demonstrating substantial advantages over traditional models. Compared to support vector machine (SVM) and random forest (RF) models, the optimized BP model exhibits marked improvements in accuracy, precision, mean squared error, and prediction time. In tests conducted on various data types, the optimized model achieves an accuracy rate of 87.3% for textual data, 88.7% for numerical data, and 86.2% for categorical data, significantly outperforming both SVM and RF models. Regarding prediction time, the optimized model processes 1,000 data entries in just 0.05 s, which is considerably faster than SVM (0.12 s) and RF (0.08 s). Moreover, the model demonstrates superior performance in mean squared error, achieving errors of 0.063 for textual data, 0.059 for numerical data, and 0.067 for categorical data, consistently surpassing the baseline models across all metrics. Based on the predictions generated by the BP model, this paper recommends strengthening collaborations between educational institutions and enterprises, expanding the scope of partnership projects, and increasing internship opportunities for students. These findings provide valuable guidance for optimizing school-enterprise collaboration models and talent cultivation strategies within tourism education. Such improvements are anticipated to enhance students' practical skills and employability while fostering innovation in the development of tourism talent. This paper also expands the interdisciplinary applications of the BP prediction model within the educational sector, offering new perspectives and methodologies for enhancing school-enterprise collaborations in tourism education. By advancing these frameworks, the paper contributes to the innovation and evolution of educational models, promoting both practical advancements and theoretical progress in the field.