This paper intends to use cloud computing technology combined with multi-source heterogeneous data to study extensive data analysis and modeling methods for dense environments. This paper aims to improve the mining and detection of massive Internet of Things (IoT) big data in the cloud environment. Firstly, relevant statistical characteristics and correlation rules are extracted from massive IoT big data. Secondly, a multi-source heterogeneous network model based on block is proposed. This paper uses a multi-source isomer model to process the collected data, and it proposes a semantic ontology decomposition method for big data in dense IoT scenarios in a cloud environment, and establishes its association rule knowledge base. Meanwhile, the multi-source heterogeneous information transmission mechanism, and then the dense IoT in the cloud environment is analyzed and mined with big data. Experiments show the proposed algorithm performs better anti-jamming when applied in dense IoT environments. This method has better mining accuracy and less time cost.