In the field of atmospheric measurement, clouds are the most uncertain factor in atmospheric models, so accurate segmentation and recognition of cloud image are indispensable. However, due to the stochastic nature of clouds and atmospheric conditions, challenges exist in the precision and accuracy of cloud image segmentation. To address this issue, we propose a novel network named CloudHS-Net based on MobileNetV2. This network incorporates a hybrid concatenation structure, dilated convolutions, and a mixed dilated design, along with an efficient channel attention mechanism, for practical cloud image segmentation. The performance of the network is thoroughly evaluated on the SWIMSEG and HHCL-Cloud datasets through comparative tests with other advanced models, providing insights into the network's performance and the roles of its various components. Experimental results demonstrate that the efficient channel attention and hybrid concatenation structures effectively enhance the segmentation performance of the model. Compared to current advanced ground-based cloud image segmentation networks, CloudHS-Net excels in the task of sky cloud image segmentation, achieving an accuracy of 95.51% and mean intersection over union (MIoU) of 89.86%. The model reduces disturbances originating from atmospheric environment, such as sunlight, pay stronger attention to cloud. This leads to enhanced precision in cloud image segmentation, allowing for a more accurate capture of cloud coverage status and the experimental results show that the method is feasible.