Research on Deep Learning Algorithm in Cultural and Creative Product Design

Abstract

Cultural and creative design is a new design mode based on the Internet platform, which gathers the design wisdom of the public and provides online solutions for design tasks. Evaluation indicators are used to score cultural and creative design schemes that require manual construction of an evaluation matrix, which makes the evaluation efficiency very low and increases the product design cycle. Therefore, based on the deep belief network of cultural creation, the method of constructing the evaluation model of the scheme is designed in this paper. According to the characteristics of the deep belief network, the k-medoids clustering method is adopted, and the clustering key value k is set. Meanwhile, the clustering result is calculated, and k central point elements are obtained. Moreover, project the central point elements back to the cultural and creative design plan to obtain the characteristic plan, and use manual evaluation to calculate the comprehensive score. Besides, the feature scheme data and the data simulation formula are used to construct the training set of the deep belief network, which are used to train the evaluation model of the deep belief network to solve the problem of the length of time that requires a large amount of manual evaluation to construct the output of the training set. In addition, according to the number of evaluation indicators, the number of nodes in the input layer of the deep belief network is determined to define the initialization method of network weights and biases, formulating experimental procedures to determine the optimal network structure. In the simulation experiment, through the test analysis of the comparison model, the function synchronization rate in this paper is 4.2% better than that of the comparison method. After multiple iterations, the test effect is 7 times higher than that of the comparison method, realizing the optimal evaluation of the design schemes that have not been manually evaluated in the same design scheme set.