# Fuzzy MLKNN in Credit User Portrait +
## 1 Introduction  
The Credit Reference Database of the People's Bank in China is a fundamental credit database in which the basic information and credit information of legal persons and organizations are collected, sorted, preserved, and processed in accordance with the law, where credit data are generally divided into corporate credit data and personal credit data [1]. An enterprise's credit report data report generally includes the basic information of the enterprise, affiliated company information, financial statements, information summary, credit history, etc., while in a personal credit report, the content generally includes basic personal information, work, and residence. Information, individual credit card information, default information, etc. [2]. With the expansion of credit reporting, the credit reporting system is constantly being upgraded. The personal education and residence information of the first-generation system has been further improved, and the loan repayment record has been increased from two years to five years. It is known as \"the most stringent credit investigation system in history\" [3]. From a macro perspective, based on credit data, through database technology and the credit analysis system, it can help banks obtain various credit reports, thereby providing a reference for credit demanders to formulate plans and identify risks. At the same time, regularly publishing a blacklist of untrustworthy enterprises, introducing honest enterprises, and releasing relevant information to the public can play a role in monitoring bad business practices and encouraging honesty and trustworthiness. Therefore, credit data is the basic link to the operation of the social credit system, and it is of great significance for the realization of the functions of the social credit system. From a micro perspective, the revealing function of personal credit data can help individuals prove their credit status in a short period, thereby helping borrowers with good credit to obtain loans quickly. For lending institutions, they can also quickly learn about lenders to help judge and control credit risks and to assist them in credit management activities on this basis [4]. Categorized from the perspective of data processing, credit data mainly includes original survey data and processed data. From the perspective of data attributes, credit data is mainly divided into quantitative and qualitative data. Qualitative data is a recordof some facts described in language form, and quantitative data can be directly used for quantitative analysis. Many qualitative data can be quantified, but credit data itself has the characteristics of many variables, a large amount of sample information, and is non-intuitive [5]. Therefore, how to intuitively obtain the user's credit information from a large amount of data has become an urgent problem to be solved. In recent years, research on data mining has emerged. Some scholars have discussed the relationship between data mining and credit reporting systems, sorted out the basic framework of applying data mining in credit reporting systems, and applied traditional data mining methods to handling credit data [6]. According to the characteristics of credit data, some scholars use the method of feature selection and other basic machine learning algorithms to process personal credit data to study the personal credit status of users [7; 8]. However, the limitation of past research is that, on the one hand, the discretization method used to process credit information data in the past process will weaken the objectivity of the data. On the other hand, the classification method of classical machine learning is used, along with a single label, such as portraits of users and \"good\" and \"bad\", which will bring about a lot of information loss. Therefore, developing a multi-label learning algorithm suitable for analyzing credit data is the key problem to solving the multi-dimensional portrait of credit users.