Research on Ceramic Art Price Prediction Based on CNN-LSTM-STL Multi-Task Model
With the rapid development of the China's economy,the ceramic art market in China has also rapidly developed.However,a systematic pricing framework has not yet been established,leading to significant price disparities.This paper utilizes 150 sets of ceramic data from Yachang Auction website.First,convolutional neural networks(CNN)are used for feature extraction,and the extracted data is input into the LSTM model.Finally,the CNN-LSTM model is used as a single task model in the multi-task model,resulting in an improved CNN-LSTM-STL multi-task prediction model.Empirical results show that the improved CNN-LSTM-STL prediction model significantly enhances the accuracy of sequence simulation and prediction,and its research results provide a basis for predicting the price of ceramic artworks.
ceramic artworksConvolutional Neural Network(CNN)multi-task predictionCNN-LSTM-STL prediction model
万方数据
维普
