Analysis of Traditional Culture Communication Methods and Communication Effects for Adaptive Web Platforms

With the rapid development of networks and information technology, the form of mass communication is changing day by day, and the efficient and convenient communication of information also brings challenges to the dissemination of traditional culture. This paper proposes a deep recommendation algorithm based on user-adaptive networks after examining the functional advantages of adaptive network platforms and optimizing the drawbacks of current network platform recommendations. It contains two parts: the main network and the strategy network, which combine the null convolutional neural network and the residual network, which is capable of better modeling the user’s historical behavior sequence. On this basis, the algorithm is combined with the development of the traditional culture dissemination-oriented network platform system, constructed by the user layer, testing layer, learning layer, support layer, and management layer composed of a traditional culture adaptive network platform. Then, the performance of the system is tested. The adaptive network platform system of this paper has the lowest number of nodes with latency distribution of [1, 3.1] ms and [4, 10] ms. The lowest load reaches 0.05, and the proportion of users with a continuity index less than 0.9 is only about 36%. Meanwhile, the average playback volume of the platform video is 6,275,800, and the average number of likes reaches 207,906, which is much higher than the average value of 8800 of the number of points stepped on, indicating that the system in this paper is optimal in all aspects of the performance and that the dissemination of traditional culture is excellent. This study presents an effective communication method for traditional culture that is widely applicable and makes a significant contribution to the promotion of traditional culture.