Anhui University of Finance and Economics, School of Accountancy, Anhui Bengbu 233030, China.
Financial Work Office of Nanchang Municipal People's Government, Jiangxi, Nanchang 330038, China.
Comput Intell Neurosci. 2022 Aug 29;2022:9923676. doi: 10.1155/2022/9923676. eCollection 2022.
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.
随着移动互联网信息技术的飞速发展，自动化搜索文本在许多行业中占据了主导地位。本文不仅对文本特征提取和分类方法的基本工作原理进行了详细的案例研究，还对提取算法及其基本概念以及文本特征分类中可能遇到的一些问题进行了深入的案例分析，并详细说明了它们的优缺点。针对各种算法的缺点，提出了稀疏贝叶斯概率模型，使其能够更好地满足数据库和文本分类的要求，并进一步提高相关技术。如今，中国商誉价值的评估，无论是在理论上还是在实践中，通常都简单地采用传统的固定资产评估方法。但是，传统方法存在忽略与同行业比较以及不考虑影响企业商誉的不同因素的缺点。本文采用了一种将传统方法与传统方法相结合的新方法来评估商誉，并试图改进这种传统方法所得到的结果，以使评估结果更加准确。然后，通过研究传统的中国风险评估和预测模型的适应性，进行了全面的比较。针对企业超额资产回报风险评估方法在实践中难以预测的尴尬局面，创新性地研究了新的灰色因素评估模型。
