The essence of cloud image prediction is spatiotemporal sequence prediction. Deep learning-based spatiotemporal sequence prediction algorithms can be categorized into three frameworks: SISO, MIMO and MISO. Based on the characteristics of cloud image movement, designed a cloud image prediction algorithm under the MISO framework, combining the features of both MIMO and SISO frameworks, called the implicit autoregressive spatiotemporal channel aggregation prediction (IASCACP) algorithm. To address the issue of image correlation loss in MIMO models and excessive error accumulation in SISO models, introduce an implicit autoregressive encoder-decoder. This encoder-decoder endows the model with certain recursive properties through an implicit autoregressive structure to capture spatiotemporal correlation information in sequences. Additionally, a masking and true mapping module is used to reduce error accumulation from recursion and enhance model robustness. To tackle issues such as instability and nonlinearity in cloud movement, we designed a spatiotemporal channel aggregation predictor. This module effectively aggregates multi-order spatiotemporal interaction information and performs adaptive channel reallocation to reduce feature redundancy. The algorithm was tested on the MovingMNIST dataset and the FY-4A satellite cloud image dataset. Experimental results show that this algorithm effectively improves the shortcomings of MIMO and SISO models and achieves higher prediction accuracy compared to other models, demonstrating its reliability and effectiveness in the field of cloud image prediction.