ABSTRACT Currently, the digital transformation of education and the professional development of teachers are at a crucial crossroads. The purpose of this research is to gain a profound understanding of the core contradictions in traditional education and to propose a systematic and engineering‐oriented technical solution. This study takes the forecasting of teacher talent demand as the research object, constructs the corresponding teacher talent demand forecasting model based on high‐resolution neural network in the edge computing environment, speculates and budgets the number of elementary school teachers in urban and rural schools based on the demand of student‐teacher ratio in 2025–2035, and proposes the optimization strategy for the cultivation of teaching skills of teachers on the basis of this. The model data show that between 2025 and 2027, the demand for elementary school teachers gradually increases, reaching a peak of 5,961,500 in 2027, and then shows a decreasing trend after 2027, reaching 4,471,800 by 2035. The supply of basic education teachers for high‐quality development is expected to undergo structural changes in the proportion of supply from different channels. Among them, the supply of undergraduate teacher training graduates will drop to 449,700, while the supply of master's degree graduates will increase to 211,500. Overall, the size of teacher training undergraduate graduates tends to stabilize, and the size of master's degree graduates gradually rises, and eventually the total number of teacher training graduates rises slightly, from an annual average of 604,700 in 2025–2030 to an annual average of 661,200 in 2031–2035 gradually. Taken together, this study provides a new method for further research on the problem of forecasting the demand for teacher personnel, which is conducive to improving the scientific nature of the research work on the development of teacher teaching skills.
Genlian Zhang (Sun,) studied this question.