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Educational ecology is a branch of pedagogy that uses ecosystem theory to study the law between education and the external ecological environment. In this paper, We use ecology research methods to evaluate the health and sustainability of the education system. Based on Educational stability, carrying capacity and teaching quality, we build the three-dimensional baseline cube model (called the Tri-E model) for comprehensive measurement. In terms of educational stability, we build educational pyramids according to different academic qualifications and calculate Gini coefficient according to geographical distribution and the Educational Stability Index (ESI) is summarized. For the carrying capacity and quality of education, the weight of each factor is determined by the AHP. The Education Carrying Capacity Index (ECCI) and Education Quality Index (EQI) are calculated according to the relevant indicators of the countries studied. These indicators will determine a vector in the 3D Baseline cube to obtain a measure of the health status of higher education Tri-E Index (TEI). We analyzed some representative countries (UK, India, and China) with TEI of 0.741, 0.521, and 0.665, respectively. Referencing China’s education reform, we put forward the current problems facing the Indian education system and how to reform it. After a quantitative analysis of the actual resistance index, the conclusion is that it is difficult for the reform to be completely successful.

A large number of by-products will occur during the transformation of ethanol to C4 olefin, resulting in the selection of the destination products that are not enough to meet industrial needs. Therefore, the selection of catalyst combinations and environmental conditions are of great significance for the preparation of C4 olefin. First of all, this paper adopts a random forest model to screen the experimental characteristics and the Grid Search is used for optimizing hyperparameters to evaluate the importance of different catalyst types to ethanol conversion rate and C4 olefin selectivity. On this basis, with the multiple regression equations established among different catalyst types, temperature and C4 olefin income as the target function, under the constraint of temperature, Co loads, Co/SiO2 and HAP quality ratio, ethanol concentration, obtain the combination of catalysts with the highest income of C4 olefin. The proportion of C4 olefin in the target product was increased. At the same time, it provides reference for exploring suitable chemical process conditions of C4 olefin.