The current evaluation index systems (EISs) of innovation and entrepreneurship (I&E) ability are not sufficiently systematic, scientific, or practical. To solve the problem, this paper tries to evaluate the I&E ability of computer majors, using neural networks improved by particle swarm optimization (PSO). Firstly, an EIS of 22 second-level indexes under 5 first-level indexes was designed to evaluate the I&E ability of college computer majors. Next, an evaluation model was developed based on fuzzy neural network (FNN), and the corresponding training algorithm was created. Moreover, an improved PSO was introduced to optimize the FNN, and the optimization process was detailed. The proposed model was proved effective through experiments.

To alleviate the urban heat island effect that is getting increasingly serious these days, the research on the monitoring, simulation, and regulation of the thermal environment of cities has become a necessity. Aiming at figuring out the correlations between influencing factors and giving accurate quality evaluation of urban thermal environment, this study extracted 10 influencing factors of urban thermal environment and gave their influence, and then performed the Land Surface Temperature (LST) retrieval of a target city. After that, this paper constructed a Multiple Linear Regression (MLR) model and explored the law of the numerical changes of the influencing factors of urban thermal environment. At last, this paper also built a BP neural network to predict the quality evaluation of urban thermal environment and used experimental results to prove the effectiveness of the proposed algorithm and model.