To address the issues of strong subjectivity, delayed feedback, and single dimension in traditional classroom teaching evaluation, and to meet the personalized and capability-oriented needs of new engineering education, this paper constructs a multi-modal intelligent classroom teaching evaluation model driven by whole-process collaboration. This model collects data from visual, auditory, operational, and behavioral aspects through a distributed sensing network. After preprocessing and feature extraction, it achieves collaborative analysis of multi-source data relying on a dynamic hierarchical and adaptive fusion framework. The model is practiced in the Java Programming Technology course, and the results show that the recognition accuracy of the multi-modal fusion model is highly consistent with teachers' manual evaluation. It can achieve full-dimensional evaluation from teaching theory to practice, providing new ideas for intelligent evaluation of programming courses in new engineering education.