In the context of "smart healthcare", due to the substantial increase in medical data and patient diagnostic needs, conventional diagnostic methods are gradually unable to meet the current diagnostic requirements. Therefore, a network medical image information feature diagnosis method based on big data is designed to improve the effect of disease diagnosis. The convolutional deep belief network is used to extract the information features of the network medical image in the network medical image. The t-SNE algorithm is used to select the more valuable network medical image information features in the extracted features. Using stacking to integrate AdaBoost and Bagging algorithm, the disease diagnosis results are obtained. The artificial bee colony algorithm is used to optimize the weights of the multi-level ensemble learning algorithm to improve the accuracy of disease diagnosis. In the multi-level ensemble learning algorithm after weight optimization, the selected network medical image information features are input and the disease diagnosis results are output. Experiments show that this method can effectively extract the information features of network medical images and accurately diagnose diseases. At different spatial resolutions of network medical images, the Kappa values of disease diagnosis of this method are high, and the lowest Kappa value is about 0.875, which means that this method has high disease diagnosis performance.

在“智慧医疗”的背景下，由于医疗数据和患者诊断需求的大幅增加，传统的诊断方法逐渐无法满足当前的诊断需求。因此，设计一种基于大数据的网络医学图像信息特征诊断方法，以提高疾病诊断的效果。利用卷积深度置信网络提取网络医学图像中网络医学图像的信息特征。采用t-SNE算法选择提取的特征中更有价值的网络医学图像信息特征。利用Stacking集成AdaBoost和Bagging算法，得到疾病诊断结果。采用人工蜂群算法优化多级集成学习算法的权重，提高疾病诊断的准确性。在权重优化后的多级集成学习算法中，输入选定的网络医学图像信息特征，输出疾病诊断结果。实验表明，该方法能够有效提取网络医学图像的信息特征，准确诊断疾病。在网络医学图像的不同空间分辨率下，该方法的疾病诊断Kappa值较高，最低的Kappa值约为0.875，这意味着该方法具有较高的疾病诊断性能。