The imbalance data refers to at least one of its classes which is usually outnumbered by the other classes. The imbalanced data sets exist widely in the real world, and the classification for them has become one of the hottest issues in the field of data mining. At present, the classification solutions for imbalanced data sets are mainly based on the algorithm-level and the data-level. On the data-level, both oversampling strategies and undersampling strategies are used to realize the data balance via data reconstruction. SMOTE and Random-SMOTE are two classic oversampling algorithms, but they still possess the drawbacks such as blind interpolation and fuzzy class boundaries. In this paper, an improved oversampling algorithm based on the samples’ selection strategy for the imbalanced data classification is proposed. On the basis of the Random-SMOTE algorithm, the support vectors (SV) are extracted and are treated as the parent samples to synthesize the new examples for the minority class in order to realize the balance of the data. Lastly, the imbalanced data sets are classified with the SVM classification algorithm. F-measure value, G-mean value, ROC curve, and AUC value are selected as the performance evaluation indexes. Experimental results show that this improved algorithm demonstrates a good classification performance for the imbalanced data sets.

With the development of mobile network communication technology, online shopping has further become the mainstream way of mass consumption. To this end, this article attempts to start from the innovation of e-commerce platform, uses today’s Internet of Things, collects relevant information, and collects relevant data through smart sensors, to establish a mobile e-commerce platform and analyze and explore the impact of e-commerce logistics customer satisfaction of factors revolve around e-commerce logistics. This article uses smart sensor technology to mine and analyze e-commerce information data and then design and build a new mobile e-commerce platform. Taking the two major e-commerce platforms of Jingdong and Taobao as examples, through online evaluation surveys, the importance of factors affecting logistics service quality and customer satisfaction under different logistics distribution models was explored and analyzed. Under JD’s selfoperated logistics distribution model, users pay the most attention to the integrity of the delivered goods, the accuracy of the delivery time, and the service attitude of the delivery personnel. The importance is ranked second, first, and third, reaching 48.36%, 50.36%, and 61.64%, respectively. Under the third-party logistics distribution model of Taobao, the main influencing factors are the integrity of the delivered goods, the accuracy of the delivery time, the importance of outer packaging, and the importance of product integrity, reaching 37.52%, 41.1%, and 24.29 %, respectively.

The process of global integration has accelerated, and the international financial market has become increasingly closely linked. The financial risks that come with them are becoming more complex and difficult to guard against. Multimedia modeling in Health Cloud biometric authentication and data management systems can be applied to the analysis of financial markets. Most of the current financial risk analysis models are based on a single time, and the models are relatively simple and cannot adapt to the current complex multidimensional mixed financial risk environment. Therefore, this paper aimed to analyze the spatial spillover effect of financial risk contagion based on the directed asymmetric (DAI) spatial econometric model. This paper proposed to transfer entropy information weight information and introduce the GARCH (generalized auto-regressive conditional heteroskedasticity) model to improve the traditional econometric model. By constructing a DAI measurement model, the spatial contagion of multidimensional mixed financial risks was analyzed, and on this basis, a generalized multidimensional economic space was established to analyze spatial spillover effects and analyze the specific path of spatial spillover effects. The model results in this paper showed that the degree of correlation between the stock and bond market varied greatly between countries. Among them, the change coefficient W s ⟶ b r s D of the event period was judged to have a large degree of negative change in the United Kingdom, Germany, and France in the European Union, which were −0.9885, −0.9876, and −0.9748, respectively. This showed that the model in this paper had a good and reliable ability to cope with the current multidimensional mixed complex financial risk environment and could be used as a reference for financial risk-related research. At the same time, it also proved that multimedia modeling in health cloud biometric authentication and data management system could provide a role in financial risk contagion analysis.

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