Entrepreneurship is an important means of economic development. Rural migrant workers returning home to start their own businesses can promote employment, alleviate poverty, and achieve rural development structural transformation of rural development. The entrepreneurial effect of rural return migrants is important for rural economic development. Using the data of the China Labor Force Dynamics Survey (CLDS thereafter) 2018 and China Household Finance Survey (CHFS thereafter) 2019, we analyze the entrepreneurial effects of return migrants upon their return to their hometowns. We construct a career choice model and build a mathematical model based on it to formulate the hypothesis. Then, we use the Probit regression model to test the hypothesis empirically. Results find that the rural return migrants can promote entrepreneurship among residents. Land circulation, human capital, and physical capital are stimulating factors in promoting the rural entrepreneurial activities of return migrants. We recommend that the government actively guide the rural return migrants to start businesses and provide security for entrepreneurial activities by upgrading various entrepreneurial elements.

Text makes up a large portion of network data because it is the vehicle for people’s direct expression of emotions and opinions. How to analyze and mine these emotional text data has become a hot topic of concern in academia and industry in recent years. The online LDA (Latent Dirichlet Allocation) model is used in this paper to train the social hot topic data of professional migrant workers on the same time slice, and the subtopic evolution and intensity are obtained. The topic development is divided into four categories, and the classification model is created using SVM (Support Vector Machine). Instead of decision makers, a virtual human with sensibility and rationality is built using a hierarchical emotional cognitive model to solve multiobjective optimization problems interactively. It analyzes human body structure and emotional signals, and then combines them with visual and physiological signals to create multimodal emotional data. An example is used to demonstrate the effectiveness of the proposed model.