Abstract: Hospital operation data needs to be synchronized across multiple systems and faces frequent access and processing, leading to issues such as data loss, errors, or duplication in the storage process, which affects the accuracy of data analysis and decision-making. To ensure data consistency and accuracy while providing efficient data access and processing capabilities, a hospital operation data cloud storage method based on deep integrated networks is proposed. Build a random forest tree using filtered hospital operation data and perform spatial dimension random extraction within the forest. Using convolutional neural networks as individual sub classifiers and combining with majority voting mechanism to form a deep ensemble network, the final sample category is determined through transfer learning strategy. Effectively identifying and filtering duplicate data in hospital operation data using Bloom filter technology, and by setting an objective function and using collaborative evolution algorithm through multiple rounds of iteration and optimization, ultimately obtaining the optimal solution for cloud storage of hospital operation data. The experimental results show that the proposed method has a throughput of over 400 MByte/ s when processing data, and its memory and CPU(Central Processing Unit) usage rates are only 10. 39% and 8. 88% when the data dimension is as high as 9 000. The proposed method has better cloud storage performance for hospital operation data, which can effectively improve the efficiency ofhospital information management and the ability of collaborative work. 

Key words: deep integrated network, hospital operation data, cloud storage, random forest, bloom filtration, coevolution