Camouflaged target recognition has significant applications in industrial, agricultural, and remote sensing domains, where hyperspectral imaging plays a pivotal role due to its rich spectral information and superior spatial resolution. However, the scarcity of datasets for hyperspectral camouflaged target recognition (HCTR) remains a critical challenge, primarily due to the high data acquisition costs and the complexity of pixel-level annotation. To address this issue, we have constructed two novel hyperspectral datasets: the Green Camouflage Net Hyperspectral Image Dataset (GHSI) and the Yellow Camouflage Net Hyperspectral Image Dataset (YHSI). These datasets were created using camouflage net materials, with data quality ensured through advanced hyperspectral imaging techniques and rigorous annotation protocols, thereby enriching the existing HSI database resources. Furthermore, we evaluated nine classical hyperspectral target detection algorithms on these datasets. The experimental results show that the camouflaged targets can be effectively identified on both datasets, confirming their suitability and effectiveness for HCTR research.