Analysis of Nomadic Civilization in Northern Grassland in Plastic Arts Based on Deep Learning

Abstract

In this era of rapid development, the exchanges between countries are increasing rapidly, which leads to the integration of multiculturalism and its impact on the local culture, making it diluted. Taking the plastic art features of the nomadic civilization in the northern grasslands as an example, the plastic art features of the nomadic civilization are very rich, including color, texture, shape, and local characteristics; the use of traditional methods will lead to poor feature effects, and it is difficult to obtain high-level information. There will also be problems with image recognition. With the hot development of deep learning, for these problems, its advantages and characteristics are introduced and applied to the characteristics of plastic arts, and a deep and shallow network is constructed as its input and feature recognition, which solves the problem of image feature recognition. At the same time, the convolution idea is introduced to enlarge its features, which is more conducive to feature recognition, extraction, and analysis. For the neural network model of deep learning, the traditional optimization algorithm is changed to the Adam optimization algorithm, which solves the problem of decreasing accuracy, improves the accuracy of prediction, and makes it more stable. From the final experimental results, it is not difficult to find that the feature algorithm greatly improves the accuracy rate under different noises, and the time consumption of the algorithm operation is also reduced. The traditional algorithm of the deep learning neural network model is changed to the Adam optimization algorithm, which also improves the prediction accuracy and makes it more stable. In the future development, the unsaturated function can be used as the activation function to optimize or change the model feature algorithm to make the model easier to build and have better training effects.