US financial media CNBC reported that Amazon sent users an email on Wednesday saying that due to technical problems, some users' names and email addresses were leaked, but the problem has been resolved. An Amazon spokesperson said in a statement: "We have addressed this issue and notified potentially affected users. "Amazon not only declined to disclose how many users are currently affected or how long user information has been exposed, nor did it say where the data was leaked, so the outside world can't estimate the severity of the matter. In the field of network and information security, various challenges are being faced. On the one hand, the security architecture of enterprises and organizations is becoming more and more complicated, and multiple types of security data are increasing. The traditional analysis capabilities are weak. On the other hand, the emergence of new threats, the deepening of internal control and compliance, and conventional analysis methods have many shortcomings; more and more security information needs to be analyzed, and decisions and responses made more quickly. Information security also faces challenges from big data.

In order to improve the style extraction and extraction ability of works of art, a color extraction method based on color features is proposed. The color feature extraction method is used to extract the style of visual works of art. The color feature region of works of art is segmented combined with a sparse scattered point reorganization method. Texture tracking and matching method is used for information fusion of works of art, combined with corner detection, three-dimensional edge contour feature detection method to realize texture filling and automatic rendering of art color extraction to improve art graphics’ color visual feature expression ability. The color feature data method is used for visual feature sampling and equalizing works of art. According to the equilibrium configuration results, the fuzzy clustering method is used to extract the color style of works of art to improve the style extraction and extraction identification ability of works of art. The simulation results show that this method has high accuracy in color extraction of works of art. It has a good effect on the extraction of art creation style and improves the ability of three-dimensional extraction and automatic extraction of art.

Music style is one of the important labels for music classification, and the current music style classification methods extract features such as rhythm and timbre of music and use classifiers to achieve classification. The classification accuracy is not only affected by the classifier but also limited by the effect of music feature extraction, which leads to poor classification accuracy and stability. In response to the abovementioned defects, a deep-learning-based music style classification method will be studied. The music signal is framed using filters and Hamming windows, and the MFCC coefficient features of music are extracted by discrete Fourier transform. A convolutional recurrent neural network structure combining CNN and RNN is designed and trained to determine the parameters to achieve music style classification. Analysis of the simulation experimental data shows that the classification accuracy of the studied classification method is at least 93.3%, and the classification time overhead is significantly reduced, the classification results are stable, and the results are reliable.

It is simple to manufacture resource fragments, waste resources, and alter the matching impact of resource performance using the balanced allocation technique of sports distance education resources. Linear prediction is used to offer a way for distributing sports distant education resources in an equitable manner. Using linear prediction, the resource demand can be calculated, and the matching model between virtual and real resources may be constructed using the performance vectors of virtual machines and servers. The balanced allocation approach for sports distance education resources was created with the goal of lowering server count, enhancing resource utilization, and balancing the use of various resources. The balanced allocation outcome is the output Pareto optimal solution set. Its average resource performance matching distance is 765, which is 284 and 465 less than that calculated using the BF and RR algorithms for 1000 virtual machines, respectively. Therefore, in terms of matching resource performance and reducing resource fragmentation, this strategy surpasses the other two.

In order to improve the efficiency of ideological and political intelligent teaching, this paper uses interactive modeling technology to build an ideological and political education platform. In the classroom interactive image processing, this paper uses the maximum circumscribed moment of the contour to extract the rectangular block area and uses the convolutional neural network to distinguish the defect and the text for the extracted rectangular block area. In addition, this paper comprehensively judges the detection results of each detection area and designs a corresponding detection algorithm for each part to detect defects. From the experimental research results, it can be seen that the interactive ideological and political teaching platform proposed in this paper has a good interactive teaching effect and can effectively improve the effect of ideological and political teaching.