Research on intelligent prediction model of big data mining and cloud computing based on deep learning

With the explosive growth of data volume, how to efficiently mine and utilize the value in massive data has become a key issue. This paper proposes an intelligent prediction model of big data mining and cloud computing based on deep learning, aiming at improving prediction accuracy and efficiency, ensuring data security and optimizing resource allocation. On the data level, the hybrid cloud storage architecture combined with Apache Arrow memory data format conversion module is adopted to realize the fusion and efficient preprocessing of multi-source heterogeneous data. Distributed feature engineering uses Spark MLlib to build a parallel pipeline and integrate data enhancement technologies such as SMOTE and MixUp. At the model level, a hybrid framework of Transformer-CNN is designed, which integrates the attention-gating mechanism, and realizes lightweight deployment through neural architecture search and knowledge distillation. In terms of privacy protection, we combine vertical federated learning and differential privacy technology, and use Paillier homomorphic encryption to ensure the security of multi-party cooperation. On the resource scheduling level, an elastic resource scheduling algorithm is designed based on deep reinforcement learning, and dynamic resource management is realized by combining Kubernetes and Istio. The experimental results show that the model performs well in financial risk control, medical imaging and traffic prediction scenarios, with AUC-ROC value of 0.951, reasoning delay of only 9.8ms, significantly shortened training time, greatly improved resource utilization and remarkable privacy protection effect. This research provides an efficient and safe solution for the wide application of artificial intelligence technology in many fields.