中文摘要：

农户信用借款信息的处理是农户信用评级中的一个重要环节.本文基于社会大数据信息研究农户信用借款声誉计算模型,分析对农户信用借款产生主要影响的七大因素：借款金额、借款期限、借款距离、联保因子、还款能力、反馈评分和近期声誉,并对这七大影响因素进行量化,然后在分别考虑农户在历史上从未借款、首次还款、当期无新还款和当期有新还款这四种不同的情形,相应建立了社会大数据信息下农户信用借款声誉计算模型,并通过两个实例对模型的应用进行有效性的检验.实例分析表明,在社会大数据信息下,采用本文所建立的农户信用借款声誉计算模型对农户声誉值的计算,结果能够很好地描述农户声誉值的变化趋势.由于这种变化趋势呈现较平稳光滑的变动,因此符合在具有社会管理功能前提下的声誉值变化特征,从而说明模型应用的有效性.该模型在实践中对农户信用借款声誉值的计算具有重要借鉴价值.

英文摘要：

Treatment of farmer credit loan information is an important problem in the credit rating. The paper mainly studies reputation calculating models on farmer credit loans based on social big data information. It analysed the seven factors affecting farmers credit loan： loan amount, loan period, loan distance, UNPROFOR factor, repayment ability, feedback rating, and recent reputation. The seven fac- tors are quantified. And then to the farmer, considering four different stations： never borrowing in the history, first repayment, no new repayment in the current, and with a new repayment in the current, it correspondingly established the credit computing models on the farmer credit loans. The two examples test the models and show that, by using the models to calculate the reputation values of the farmer credit loans, the results can well describe the change trend of the farmer＇s reputation values about his credit loans. Because the change trend is more smooth movements, which just fits in the change characteristic of the reputation value with the premise of the social function. Therefore, the model is proved to be valid application. The models in the practical work have the reference values to calculate the reputation values of the farmer credit loans.