This paper analyzes inflation forecast based on BP neural network model. Firstly, it reviews some references about BP neural network and finds that it is a nonlinear adaptive data-driven model with induction ability and a wide range of function approximation ability so that BP neural network could be applied into forecast research. Secondly, it builds up the BP neural network model to predict CPI, selecting the four indicators, which are excess liquidity, exchange rates, inflation expectation and macro-economic leading index. Then it carries out empirical experiment and takes advantage of the monthly data of the above four indicators from March 2005 to December 2012 to forecast CPI. The results show that when prediction period is 3 months, the maximum absolute error between forecast value and real value is 0.0139, and the minimum absolute error is 0.0005. When prediction period is 6 months, the maximum absolute error is not more than 0.02. It proves that BP neural network model can predict coming CPI trend at least 6 months according to the existing data and it means it is suitable for the study of inflation forecast.

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This paper analyzed personal credit evaluation system in rural area based on decision tree model. Firstly, it reviewed some references about credit evaluation methods and found that decision tree was a linear adaptive data-driven model with induction ability and a wide range of function approximation ability so that it could be applied into personal credit evaluation. Secondly, decision tree classified data samples consisting of two phases: constructing decision tree model and then classification stage. The first stage was to train data samples to establish a decision tree, and this process was divided into three steps which included feature selection, node splitting and tree pruning. The second stage was to put test samples into the established decision tree, and let it to classify from a new set of data. After that, it took advantage of the model to evaluate personal credit and selected the twenty indicators. The results showed that household assets, net assets and the existing current account balance were the most important three indicators for evaluating personal credit in rural area.