Investigation on the Identity Construction of Young Foreign Language Teachers in Colleges and Universities Based on Feature Selection Algorithm

With the gradual implementation of the strategic goal of strengthening the country in education, people’s attention to students’ learning is also increasing day by day. At the same time, the teaching effect of individual teachers depends to a large extent on the identity of teachers themselves, so it is necessary to improve the construction of teachers’ identity. Most of the research on teachers’ identity construction starts from the theory of identity, which does not play a significant role in the critical application in practical teaching activities. In response to these problems, this paper will use the ReliefF algorithm and the Spark algorithm in the feature selection algorithm to scientifically process the identity construction, and implement the application of the improved ReliefF feature selection algorithm and the feature selection application steps based on the Spark algorithm respectively. The experimental results show that the improved ReliefF algorithm has better feature selection accuracy when the feature range is 30% to 40%. It shows that the construction of teacher identity based on feature selection algorithm can provide an objective basis for the realization of teacher identity.