As a new data analysis tool, data mining has developed rapidly. Various types of data sets can be used as data mining objects. Faced with more and more data sources and larger amounts of data today, data mining can effectively obtain valuable information from them and can enable people to better predict possible data information in the future, thereby improving work efficiency. Pheromone update uses a combination of global update and local update. The improved algorithm can balance the relationship between ants’ exploration and development in the process of searching rules. The government’s macroentrepreneurial policy has an important impact on college students’ entrepreneurship. Whether the entrepreneurship can obtain government financial support, whether the entrepreneurial process is simple and easy to do, etc., all affect the enthusiasm of college students to start a business and the success of the business. As an important place for college students to live and study, colleges and universities play a vital role in the growth and future development of college students. Major colleges and universities should improve entrepreneurship education, provide materials and manpower such as teaching materials and teachers, and improve the entrepreneurial quality of college students. In addition, colleges and universities should actively establish and improve the entrepreneurial practice platform for college students to help students gain entrepreneurial experience from practice. As the backbone of the social economy, enterprises should play an appropriate role in college students’ entrepreneurship. On the one hand, they should actively provide entrepreneurial funds, and they should be willing to give college students experience guidance. Only with the joint efforts of the government, universities, and enterprises, can it be possible to establish a practical and effective support system for undergraduate entrepreneurship, promote undergraduate entrepreneurship, and increase the success rate of undergraduate entrepreneurship.

In this paper, the IoT-based adaptive mutation PSO-BPNN algorithm is used to conduct in-depth research and analysis of the entrepreneurship evaluation model for college students and practical applications. This paper details the principle, implementation, and characteristics of each BP algorithm and PSO algorithm. When classifying college students’ entrepreneurship evaluation based on BP neural network, because BP algorithm is a local optimization-seeking algorithm, it is easy to fall into local minima in the training phase of the network and the convergence speed is slow, which leads to the reduction of classifier recognition rate. To address the above problems, this paper proposes the algorithm of PSO optimized BP neural network (PSO-BPNN) and establishes a classification and recognition model based on this algorithm for college students’ entrepreneurship evaluation. The predicted values obtained from the particle swarm optimization neural network model are used to calculate the gray intervals, and the modeling samples are further screened using the gray intervals and the correlation principle, while the hyperspectral particle swarm optimization neural network model of soil organic matter based on the gray intervals is established afterward; and the estimation results are compared and analyzed with those of traditional modeling methods. The results showed that the coefficient of determination of the gray interval-based particle swarm optimization neural network model was 0.8826, and the average relative error was 3.572%, while the coefficient of determination of the particle swarm optimization neural network model was 0.853, and the average relative error was 4.34%; the average relative errors of the BP neural network model, support vector machine model, and multiple linear regression model were 8.79%, 6.717%, and 9.9%, respectively. The average relative errors of the BP neural network model, support vector machine model, and multiple linear regression model are 8.79%, 6.717%, and 9.468%, respectively. In general, the entrepreneurial ability of college students is at a good level (83.42 points), among which the entrepreneurial management ability score (84.30 points) and entrepreneurial spirit (84.16 points) are basically the same, while the entrepreneurial technology ability is relatively low (82.76 points), and the evaluation results are further verified by the double case analysis method. The current problems encountered by university students in entrepreneurship are mainly the lack of practicality, which indicates that universities, industries, and national strategy implementation levels are not sufficiently focused and collaborative in entrepreneurship development to varying degrees.