This study aims to improve the quality of college financial management and reduce the risk of college financial management, and a college financial system based on multiscale deep learning is designed in this paper. This paper designs a university financial system based on multiscale deep learning. In the hardware design, the system adds multiple sensors and scans all the information in the financial database using a coordinator. In the software design, the weights that can connect the financial information of the same attribute are set by establishing a database form; according to the multilayer perceptual network topology, a full interconnection model based on multiscale deep learning is designed to realize the system’s deep extraction of data. The experimental results show that the financial risk is based on the risk warning capability for university finance, and compared with the system under the traditional design, the university finance system designed at this time has the most categories of financial information parameters extracted.
Taking the mixed cross-sectional data of large and medium-sized industrial enterprises in Shanghai from 2014 to 2021 as the research sample, this paper empirically analyses the impact of government subsidies and R&D investment on the high-quality development of the manufacturing industry. First, the quantile regression model is established to analyse the relationship among the three factors, and the asymmetric linear loss function is introduced to obtain the point estimation of quantile. According to the Moivre–Laplace limit theorem, the asymptotic distribution is obtained; the sample quantile function is calculated and the fitting residual of quantile regression is used to estimate the asymptotic covariance matrix. The model was tested by goodness-of-fit criterion; it can more comprehensively describe the characteristics of distribution, so as to get a comprehensive conclusion. The experimental results show that: with increase of the quantile, the advantage gradually increases. Medium-sized enterprises have a less significant ‘inverted U’ relationship at 0.8, while large enterprises have a more significant ‘U’ relationship at 0.2–0.5. It can effectively reduce the estimation deviation and reduce the root mean squared error, so as to improve the estimation accuracy.
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