Construction and Empirical Analysis of NFT Digital Art Market Value Prediction Model

Abstract

The purpose of this article is to build and verify an effective market value prediction model of NFT digital art and make an empirical analysis to cope with the growing demand and uncertainty of NFT market. Through the comprehensive application of data collection, preprocessing, neural network model construction and optimization, the market value of NFT digital works of art is deeply explored in this article. In the research process, firstly, a large number of transaction data are collected from the open NFT trading platform, and strict data cleaning and feature selection are carried out; Then, a prediction model is built based on CNN (Convolutional Neural Network) model, and the model is optimized by cross-validation and parameter adjustment. The experimental results show that the model has high prediction accuracy and reliability, and can capture the key influencing factors of NFT digital art market value, such as artist reputation, scarcity of works and market demand, and point out the importance of the key influencing factors. At the same time, based on the research results, this article puts forward policy suggestions and practical enlightenment for NFT digital art market participants and regulators, with a view to promoting the healthy development of the market.