Under the background of artificial intelligence, learning analytics and educational data mining continuing to enter the foreign language teaching scene, the reform of private undergraduate German curriculum has the basis of process data collection, feature modeling and quantitative evaluation. Focusing on the evaluation needs of course goal achievement, classroom interaction quality, language ability growth and teaching support adaptation, this paper constructs an evaluation model for the effect of private undergraduate German course reform in the AI-enabled context. The classroom behavior records, assignment texts, test scores, platform access trajectories and feedback data in 4120 effective samples are uniformly cleaned, coded and associated mapped. The model consists of three parts: multi-source data representation, key feature modeling and evaluation output mechanism, and combines attention weighting, gated fusion and hierarchical scoring methods to complete reform effect identification and difference analysis. The experimental results on the validation set show that the evaluation accuracy of the model is 92.7%, the Recall is 91.4%, the F1 score is 90.9%, and the average output delay is 1.6 seconds. The model can reflect the effect and change characteristics of the German curriculum reform more stably, and can simultaneously show the association changes between vocabulary training, oral interaction, writing revision and stage evaluation. It provides continuous calculation basis and quantitative reference for course content adjustment, teaching method revision and learning support configuration.
