<p>Classroom behavior recognition in complex educational environments poses significant challenges due to occlusions, multi-scale interactions, and fine-grained behavior recognition. To address these limitations, we propose CBHA-DETR, a novel multi-kernel attention and deformable fusion network optimized for real-time behavior detection in classroom monitoring scenarios. The framework integrates a hybrid architecture featuring a Progressive Kernel Inception Block for Classroom (PKICBlock) and Monte Carlo Attention Block (MoCABlock) within its deep backbone network layer. These components synergistically enable multi-scale feature extraction through progressive parallel depthwise separable convolutions and stochastic attention mechanisms, effectively capturing spatial-contextual relationships from local movements to full-body postures. And we exclusively apply the Transformer encoder to the final layer of the backbone network to significantly reduce parameter complexity. A deformable cross-scale fusion neck (DCFusion) adaptively aligns multi-scale features via deformable convolution and content-aware upsampling (CARAFE), significantly improving geometric adaptability to complex postures. The predictions from the Transformer decoder are optimized using the Normalized Wasserstein Distance (NWD) and shape constraint metrics, enhancing geometric perception for asymmetric postures. Extensive experiments on SCB-Dataset3 demonstrate competitive performance. Specifically, our model achieves 73.2% precision and 70.9% recall with 73.4% mAP₅₀, surpassing RT-DETR-R34 by 1.0% precision, 1.9% recall and 2.0% mAP₅₀ while reducing parameters by 38.7% from 31.3M to 19.2M and maintaining real-time inference at 40.8 FPS, providing a practical and scalable solution for deployment in intelligent education systems.</p>