Internet simultaneous services of large-scale users will lead to server overload and information failure. Static content\nrecommendation system cannot adapt to the dynamic similarity characteristics of users. So, how to perceive the high\naccuracy of recommendation scheme in dynamic environment becomes one of the key techniques in application of\neducational information and embedded application. We analyze the problem of low efficiency and high error of the\nrecommendation technology based on the user�s requirement. And, we proposed the cooperative filtering\nrecommendation system based on the dynamic similarity of different users. In order to improve the prediction accuracy\nof cooperative filtering algorithm, the user�s target content would be processed with crowd scheme. Then, the system is\nfused with the recommendation system. According to the weights of the fusion, the crowd recommended fusion\nscheme are proposed. The experimental results show that the fusion mechanism of cooperative embedded filtering and\ncrowd content recommendation has obvious advantages in terms of content recommendation accuracy, reliability, and\nconvergence speed.
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