Multifeature data integration technology uses computer technology to automatically analyze and process the observation data obtained by multiple sensors in chronological order according to a defined threshold. And it can complete the necessary decision-making and evaluation tasks in the data processing process. This article is aimed at studying the effect of multifeature information fusion on dance movement recognition technology. The article mainly adopts the principle of structural risk mitigation and the image thresholding method to effectively obtain the binarized image of the dancer’s moving image, so as to select an appropriate threshold, use the threshold as its distinguishing standard, and then segment the pixel threshold. The method of multifeature information fusion and dance movement recognition technology is introduced. The performance of image classification algorithms is compared, and the experiment of dance video movement, human behavior recognition, and gait phase recognition are carried out. The experimental data show that in the power identification algorithm of this article, when the peak value is 1000 bits, the experimental data is 256 bits, the analysis and comparison data is 457 bits, and the experimental environment is 812 bits. When the peak value is 2000 bits, the experimental data is 451 bits, the analysis and comparison data is 635 bits, and the experimental environment is 947 bits. The experimental data is 562 bits, the analysis and comparison data is 546 bits, and the experimental environment is 774 bits. The experimental results show that the dance action recognition technique based on multifeature information fusion is studied well.

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We propose a new lifting method based on parallel vector message passing (PMP) called LPMP to generate quasi-cyclic LDPC codes, which can derive a (BER) performance improvement up to 0.4dB.

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