Cloud detection of remote sensing satellite data is a crucial component in the processing of remote sensing images. To address the issue of low accuracy in detecting broken-clouds and thin-clouds, this paper proposes a novel cloud detection method that utilizes high-order semantic-guided decoding and adaptive convolutional encoding. The method leverages the spatial distribution relationship between the main cloud and broken-clouds by introducing an adaptive convolutional encoder to extract correlation information between the main cloud clusters. A high-order semantic-guided decoding module is then utilized to decode semantic features, thus restoring high-resolution cloud mask images. Moreover, a dynamic fusion loss function is designed to calculate the weight by dynamically computing the missed and wrong pixels in the prediction, guiding the network to focus on broken-clouds and thin-clouds, features, thereby enhancing the overall accuracy. Experimental results demonstrate that the proposed algorithm achieves an accuracy of over 96.5% and an intersection over union of over 88.1%, effectively detecting broken-clouds and thin-clouds.