Sparse Bayesian Model and Artificial Intelligence in Enterprise Goodwill Evaluation and Dynamic Management

Abstract

With the rapid development of mobile Internet information technology, automated search text has occupied a leading position in many industries. This article not only makes a detailed case study on the basic working principles of text feature extraction and classification methods but also makes in-depth case analysis on the extraction algorithm and its basic concepts as well as some problems that may be encountered in text feature classification and explained their advantages and disadvantages in detail. Aiming at the shortcomings of various algorithms, a sparse Bayesian probability model is proposed, so that it can better meet the requirements of database and text classification and further improve related technologies. Nowadays, the evaluation of China’s goodwill value, whether in theory or in practice, usually simply adopts traditional fixed asset evaluation methods. However, traditional methods have the disadvantages of ignoring comparisons with the same industry and failing to take into account different factors that affect corporate goodwill. This article adopts a new method that combines traditional methods to evaluate goodwill and tries to improve the results obtained by this traditional method to make the evaluation results more accurate. Then, by studying the adaptability of traditional Chinese risk assessment and forecasting models, a comprehensive comparison is made. Aiming at the embarrassing situation that the current methods of corporate excess asset return risk assessment difficult to predict in practice, the new gray factors evaluation models are creatively studied.