Resumen de Application of cloud accounting in enterprise financial forecasting and decision making in the era of big data
In order to accurately understand the economic development of enterprises and increase the company's economic benefits,a study on financial forecasting and decision-making in big data cloud accounting enterprises is proposed. Enterprisesimprove the efficiency of data utilization by acquiring information processing and analysis, establishing a diversifiedcontrol mechanism, and improving the effectiveness of financial and tax management. The objective function is optimizedusing a structured sparse induced parametric number to calculate the data block centers to describe the data objects morecomprehensively and make the obtained clustered financial results more accurate. Adding classifiers to the set of labeledsamples and constraining the joined samples belonging to the wrong class combine multiple kernels from differentperspectives to obtain a comprehensive measure of similarity. Selecting sub-kernel functions and parameters to constructmultiple kernel functions, the learning and generalization capabilities of kernel functions, and using high-dimensionaldata feature vectors to construct a shared hidden subspace to maximize the similarity between prediction samples andassign greater weights in the multi-perspective clustering process for corporate financial forecasting and decision making.The analysis results show that using data clustering cloud finance, financial data can be collected and corrected promptly,and the budget accuracy is up to 90%, which provides important help to enterprise financial decision-making
