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With the rapid growth of the global population and the increasing urbanization, the urban landscape in China is gradually enriched, and the scale of the landscape that plays a healing role is expanding. However, curing the problem of landscape ecological security is an important part of Homeland security, economic and social sustainable development. We must deal with the relationship between high-quality social development and ecological environment protection on the basis of scientific evaluation. To address this issue, research has provided better data support for feature extraction through image preprocessing. Then the Convolutional neural network in deep learning is trained through a large number of collected measured data. Finally, the pressure state response model is used to evaluate the ecological security of the healing landscape. The results show that the average error of the ground class in 2010 was 13.65%, and the fitting accuracy reached 86.35%, indicating that this method has high accuracy and can be effectively applied in evaluation. Meanwhile, in 2010 and 2019, the average landscape ecological security levels of City A were 7.27 and 6.65, both at a "safe" level, but the overall security level showed a downward trend. It is recommended to optimize the land use pattern in future urban planning and construction, improve the urban landscape ecological security index value, and maintain consistency with the actual situation of the city. This can provide reference for the evaluation model of urban landscape ecological security, and further provide scientific basis and guidance for the ecological civilization construction of urban agglomerations. In subsequent research, the evolution trend of urban landscape ecological security can be taken as the research goal, and finally, guidance on optimizing urban landscape ecological security can be provided. [ABSTRACT FROM AUTHOR]

How to analyze and predict the consumption behavior of Chinese residents is of great significance based on the existing consumption theory research and the actual situation in China. This study studies the factors influencing the consumption of urban and rural residents in China through factor analysis and builds a model of urban and rural residents' consumption expenditure based on BP neural network. At the same time, through the data collection, the consumption data of urban and rural residents in China from 1986 to 2017 were collected. The BP neural network prediction model was used to predict the consumption expenditure of urban and rural residents, and the absolute error and relative error were obtained. In addition, this study establishes a combined forecasting model based on Lasso regression analysis and BP neural network to predict the consumption expenditure of urban and rural residents and compares its prediction results with BP neural network prediction model. The research shows that the combined prediction model based on Lasso regression analysis and BP neural network comprehensively surpasses BP neural network prediction model in prediction accuracy, and it has certain effectiveness and puts forward practical suggestions, which provides theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]