Diabetes mellitus is a common chronic noncommunicable disease, the main manifestation of which is the long-term high blood sugar level in patients due to metabolic disorders. However, due to excessive reliance on the clinical experience of ophthalmologists, our diagnosis takes a long time, and it is prone to missed diagnosis and misdiagnosis. In recent years, with the development of deep learning, its application in the auxiliary diagnosis of diabetic retinopathy has become possible. How to use the powerful feature extraction ability of deep learning algorithm to realize the mining of massive medical data is of great significance. Therefore, under the action of computer-aided technology, this paper processes and analyzes the retinal images of the fundus through traditional image processing and convolutional neural network-related methods, so as to achieve the role of assisting clinical treatment. Based on the admission records of diabetic patients after data analysis and feature processing, this paper uses an improved convolutional neural network algorithm to establish a model for predicting changes in diabetic conditions. The model can assist doctors to judge the patient’s treatment effect by using it based on the case records of inpatient diagnosis and treatment and to predict the risk of readmission of inpatients after discharge. It also can help to judge the effectiveness of the treatment plan. The results of the study show that the model proposed in this paper has a lower probability of misjudging patients with poor recovery as good recovery, and the prediction is more accurate.

As a typical disease, cardiovascular and cerebrovascular diseases cause great damage to the human body. In view of the problem that the existing models failed to describe and represent the characteristics of cardiovascular and cerebrovascular indicators, convolution neural network was used to analyze the metabolic factors of cardiovascular and cerebrovascular. Based on convolutional neural network theory, feature extraction was carried out on the relevant parameters of the model, and the change trend of different cardiovascular and cerebrovascular indicators was studied by model optimization, theoretical analysis, and experimental verification. Relevant studies show that the value of neurons increases slowly at first and then rapidly with the increase of bias term b . And with the increase of computing time, the corresponding nonlinear characteristics are gradually reflected; so, the influence of computing time on neuron results should be considered when selecting bias term b . The gradient changes under different functions have typical symmetry, which indicates that the effects of functions on model parameters have certain cyclic characteristics. Among them, ReLU function has the largest variation range, tanh function has a relatively small curve variation range, and sigmoid function has the smallest variation range. Five indicators are selected to describe the metabolic characteristics of the disease through characteristic analysis of cardiovascular and cerebrovascular diseases. The onset signs have the greatest impact on cardiovascular and cerebrovascular diseases, while the corresponding metabolic characteristics have the least impact on cardiovascular and cerebrovascular diseases. The study showed that the influence of different indicators on the model had typical stage characteristics, and relevant data were used to verify the accuracy of the model. Finally, the optimization model based on convolutional neural network was used to predict the metabolic characteristics of cardiovascular and cerebrovascular diseases. Relevant studies show that the optimization model can better analyze the metabolic characteristics of cardiovascular and cerebrovascular diseases. This research can provide theoretical support for the application of convolutional neural networks in other fields.

Under the background that blockchain and intelligent interconnection system are developing steadily, at the same time, our operational requirements for intelligent interconnected systems are becoming higher and higher. While urgently requiring intelligent interconnected systems to work more comprehensively, we add blockchain technology that can bring more possibilities to optimize the functions of intelligent interconnected systems and help solve the shortcomings of intelligent interconnected systems. Our optimization methods for intelligent interconnection system under blockchain include virtual blockchain tie line method, blockchain tearing method, simulation model construction method, data transformation control, TG algorithm, and PI model algorithm. The advantages are as follows: (1) we can divide the blockchain intelligent interconnection system into multiple areas and multiple tie lines by using the virtual blockchain tie line method, and finally greatly improve the information receiving efficiency and analysis efficiency of the intelligent interconnection system by numbering the whole area. (2) Using blockchain tearing method and simulation model building method, we can divide the blockchain system with large nodes into multiple areas, adjust the amount of data that can be processed in the blockchain data output capacity allocation area according to this value, and improve the functional control performance of each part. (3) We use the data transformation control method and TG algorithm to accurately obtain the global optimal solution of the path data of the intelligent interconnected system, and improve the accuracy while computing resources and time faster, and complete the optimization process of the blockchain for intelligent connection at a faster speed, which makes the optimization decision of the intelligent interconnected system of the blockchain more suitable.