Energy consumption in universities has steadily risen in recent years due to the continuous expansion of school infrastructure. This trend highlights the urgent need for effective energy evaluation and conservation. To address this challenge, this paper proposes a CNN-BiGRU-Attention (CBA)-based energy efficiency evaluation method for university integrated energy system. First, we construct an energy efficiency evaluation index system for universities from four dimensions: energy consumption, environment, economy, and reliability. We then propose a method to calculate energy efficiency values based on the improved combined weights assignment and TOPSIS method. This method strengthens the data foundation for model training by optimizing weight allocation and data fusion. Furthermore, we propose an energy efficiency evaluation method based on CBA. This method combines spatial feature extraction, time series data processing, and dynamic weight allocation to comprehensively evaluate the energy efficiency of the university integrated energy system using raw data from electricity load, cooling load, heating load, etc. The experimental results show that the proposed model improves R2 by 1.341% and reduces RMSE by 1.657 on average across the four dimensions compared to CNN. The model leads to a more accurate and comprehensive evaluation of university energy efficiency.