Influence of soil rock mixture in mountain area based on machine learning and psychological intervention of left-behind children

Abstract

With the rapid popularization of the Internet plus China and China’s rapid economic development, machine learning is playing an increasingly important role in model research. This paper presents a cold start method based on a hybrid Dirichlet process model and isolated forest. As we all know, the soil rock mixed slope is between the slope of soil and rock, most of its structure is loose and has large porosity, high permeability, and poor slope stability. Compared with soil slope and rock slope, the research on soil rock mixed slope is not comprehensive enough. Based on the study of geological environment conditions of highway slope, the mechanical properties of soil rock mixed slope are tested by traditional boundary equilibrium method and numerical simulation method. The stability of the three-dimensional slope is systematically studied in this paper. Nowadays, the mental health of left-behind children is still the focus of social attention. In this paper, a special school for left-behind children in a city is selected as the research object. The school is located between urban and rural areas. Since the children who stay in the school are from urban and rural areas, they will be selected as the research objects. Compared with ordinary children, left-behind children are more likely to have concerns, and their psychological changes are difficult to predict. What is more worrying is the long-term mental health problems. This paper aims to find the correlation between sports games and left-behind children’s mental health by introducing sports games in order to provide some methods and theoretical reference materials for solving the mental health problems of left-behind children.