Currently, the research on academic early warning assessment and prediction for college students under the credit system in colleges and universities is mainly based on methods such as machine learning. However, the existing prediction models often have problems such as difficulty in network structure, parameter selection, and extraction of context time series information. In response to these issues, this study, based on students’ historical academic performance, proposes LTBoost, a novel framework of the XGBoost classification prediction model. The proposed model framework integrates the BiLSTM module to handle the time series information in the original data. It also integrates the proposed Enhanced Transformer module to perceive global information and obtain enhanced features. Through experiments, the LTBoost prediction model was compared with four other machine learning algorithms. The accuracy of the proposed LTBoost classification prediction model increased by 0.7% to 99.4%, demonstrating a good prediction effect on whether students are at risk of prolonging their studies. It provides a new paradigm and path for the construction of talent cultivation plans in credit-based universities.