Hyperspectral Image (HSI) change detection is a key research topic in the field of remote sensing. Existing HSI change detection methods often overlook the potential interactions among training samples. To address this issue, we develop a novel HSI change detection network, the Cross-Sample Slot attention-based Network (CSSNet). This network, building on the slot attention mechanism, can explicitly distinguish between changed and unchanged region representations and disentangle these representations by multiple independent concepts. These concepts are instrumental in capturing the uniformity and diversity in the representations among different samples during batch processing. Furthermore, we introduced a Dual Gated Feed-forward Network (DGFN) to effectively filter out redundant and irrelevant information. Experimental results on two different HSI datasets demonstrate that CSSNet outperforms several existing mainstream methods in performance.