The results reveal that data mining technology and neural sites have actually high precision in forecasting future customer buying power information. The correlation coefficient between real consumption information and predicted consumption data achieved 0.9785, and also the maximum general average error was only 2.32%. It fully demonstrates information mining technology can buy some unrecognizable relevant information and future consumption trends in e-commerce methods, and neural communities also can predict future consumption power and usage patterns well.With the introduction for the big information age, the combination of data technology and training has grown to become an important way for the introduction of the business. The large-scale understanding of teaching tasks underneath the background of information data needs the forecast and analysis of learners’ faculties, behavior, and development trend. In line with the preceding circumstance, this report makes use of discrete dynamic modeling technology in huge data environment to analyze the students’ behavior in real knowledge training. By quantifying the learning process data, the feature points of each learner tend to be removed to realize the personalized construction of dynamic discovering spleen pathology information. As a result of rapid improvement system technology, we primarily review the online knowledge platform and explore the influencing elements of learners’ behavior faculties from numerous aspects. Finally, it carries down dynamic modeling and prediction for physical knowledge students from the part of Expanded program of immunization achievement modification, uses the grey model to construct the success change system, and combines the dynamic modeling technology to reflect the development trend of success. The outcomes show that the key element affecting students’ behavior improvement in physical training is video learning. Many pupils are passive and lack effort within the learning process. Discrete dynamic modeling technology can improve accuracy of forecasting pupil success modifications and provide efficient data when it comes to study content.Nowadays, the info handling capabilities and resource storage space abilities of computer systems have already been significantly enhanced, that also provides help when it comes to neural community technology. Convolutional neural networks have actually great characterization abilities in computer vision tasks, such as picture recognition technology. Intending during the issue of large similarity picture recognition and category in a specific field, this report proposes a top similarity picture recognition and classification algorithm fused with convolutional neural communities. Initially, we extract the picture surface functions, train different kinds, and different quality image sets and determine the optimal surface different parameter values. Second, we decompose the image into subimages in accordance with the texture huge difference, extract the vitality top features of each subimage, and perform category. Then, the feedback image function vector is changed into a one-dimensional vector through the alternating 5-layer convolution and 3-layer pooling of convolutional neural networks. With this basis, different sizes of convolution kernels are widely used to Tetrazolium Red in vivo draw out various convolutions for the image features, then utilize convolution to attain the feature fusion of various dimensional convolutions. Finally, through the rise in the range instruction and the boost in the actual quantity of data, the system parameters are continuously enhanced to enhance the category accuracy when you look at the education set and in the test set. The particular accuracy associated with loads is verified, as well as the convolutional neural community model with the greatest category accuracy is obtained. In the test, two picture information sets of gems and apples are selected due to the fact experimental data to classify and recognize treasures and discover the foundation of oranges. The experimental results reveal that the typical identification accuracy for the algorithm is more than 90%.Players in contemporary basketball have a lot of actual contact, lots of bumps, and lots of real battles. Your competitors for the baseball, whether floating around or on the floor, is brutal, placing greater demands on the people’ actual abilities. Mentors frequently use plateau real training, which can be very effective in establishing athletes’ cardiopulmonary purpose, among a number of other education practices. The proportional length and energetic area of arms tend to be obtained utilizing the skin color model of the human body, the angle and pose information of each and every joint is extracted from characteristics, therefore the 3D posture of hands and powerful hands is trained and recognized in this report, which is centered on Kinect. The findings revealed that moderate hypoxia in the plateau considerably lowered basketball players’ performance and therefore basketball players’ optimum heartrate and 1-minute heartbeat data recovery in high-intensity workout had been lower than those who work in flat area training.The rapid development of Asia’s regional ecommerce in addition to constant enhancement of the business design have-not only pushed the country towards the forefront of the globe additionally exposed the unprecedented possibility of China’s cross-border e-commerce.