Recently, With the development of global agricultural industrialization, modern agriculture has the functions of improving the quality of the ecological environment and providing people with the functions of sightseeing, leisure, and vacation. How to take the user as the center and combine the user’s personalized characteristics to offer the rural tourism products they need has become a research problem with real-world application value and challenge. In this context, this study develops an intelligent recommendation model by extensively analyzing the contents of rural tourism information platforms and product recommendation factors, as well as a rough set algorithm and traffic classification. To minimize the attributes of rural tourism product information and extract the core attribute, an attribute reduction approach based on a different matrix is implemented. Moreover, user interest similarity is computed and ranked to recommend rural tourist products. In addition, a personalized tourism attraction recommendation model is presented based on geographic area and period. The model achieved the highest average accuracy of 0.87%. The relevant experimental test results reveal that the system can provide accurate recommendations and services for rural tourism products.

The angiography image enhancement technology has the potential to enhance the vascular structure in the image while suppressing the background and nonvascular structures simultaneously. This technology has the ability to enhance the result as close to the real structure of blood vessels as possible. Angiographic image processing is one of the essential contents in the field of medical image processing and analysis. However, the existing cardiovascular angiography schemes suffer from various issues. In this paper, the detection process of cardiovascular angiography is studied by combining the Internet of Things and rough set technology. Firstly, this paper designs the architecture design of the cardiovascular angiography process combined with the Internet of Things technology. Secondly, this paper uses a rough set algorithm to optimize the background noise and boundary shrinkage because of the sensitivity of the contrast background noise and boundary shrinkage. Simulation results verified the applicability and efficiency of the proposed model in the cardiovascular angiography scheme. The model has been optimized during implementation. Compared with the traditional algorithm, the same image data processing speed is significantly improved to ensure the enhancement effect.