Main Article Content
Chinese Traditional Culture Teaching (CTCT), Higher Vocational Education, Artificial Intelligence (AI), Multi-Gradient Long-Short Term Memory (MG-LSTM), Origin tool
Chinese Traditional Culture (CTC) teaching, among the most significant abilities in higher vocational education, significantly impacts the knowledge of vocational college students. The current focus of vocational education was on better cultivating students' CTC understanding and integrity competency through these activities. There should be a strong emphasis in vocational education on developing students' cultural competency by helping them better understand the value of CTC and the alternative stages in which it can be used. This will help students become more comfortable interacting with people while also helping them better understand China's values. This paper presents a novel hybridized long-short term memory and recurrent neural network (Hybridized LSTM-RNN) to predict the capability of vocational education students. First, collected datasets are standardized through the normalization technique in pre-processing stage to eliminate unwanted errors. Then, Artificial Intelligence (AI) technology is used in the CTC teaching application. The proposed approach is applied in the prediction stage. This approach's performance metrics are examined and compared with certain standard techniques to obtain this research with the greatest effectiveness. The findings of this research are accomplished by employing the Origin tool.