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With statistical forecast becoming more and more mature at home and abroad, the predicting theory and methodology has developed in depth to reach a relatively comprehensive stage. In this paper, a grey prediction model is established and applied in the analysis and prediction of China’s household consumption. As the calculation result of the evaluation instance in this paper indicates, the model proposed is applicable for the analysis and prediction of household consumption with a relatively high accuracy. Introduction The household consumption is a form of monetary measurement which is a significant indicator that reflects the living standard as well as the material and cultural needs of people. It is also the quantized representation of total consumption of all the residents. Economic growth is mainly promoted by consumption demand, investment demand and export demand. Among the three factors, consumption demand has a long-lasting impact on economic growth. Under circumstance of global economy growth slowing down, expanding domestic demand and increasing household consumption has become the primary force to promote economic growth. Among the numerous factors that affect GDP, consumption contributes to GDP the most. It can be inferred from historical data that economic growth in our country would be greatly restrained once the contribution by consumption to GDP declines to be less than 50%. Therefore, consumption must be highly valued and promoted to develop the economy. Studying the existed theory and methodology and creatively applying them into social practice is the basic idea of this paper. Grey prediction[1] is the predicting process for system which contains both certain and uncertain information. It is usually carried out following the steps below. Firstly, identify the dissimilarity among the system factors and conduct cor-relativity analysis, generating a regular data sequence. Secondly, establish the differential equation model. Thirdly, validate the model. Finally, if the model passes the validation then it can be used for prediction. The grey prediction has the following advantages: easy to use, generally applicable and highly accurate. The establishment of the (1,1) GM model To avoid the stochastic characteristics of the original sequence and provide useful information for the model, the original sequence is usually processed before establishing the grey prediction model. The sequence is generally processed by conducting a continuous sum calculation[2]. Assume that there are n points in the sequence (0) X : { } (0) (0) (0) (0) (1), (2), , ( ) X X X X n = L (1) A new sequence is generated by conducting a continuous sum calculation { } (1) (1) (1) (1) (1), (2), , ( ) X X X X n = L (2) The differential equation for model (1,1) GM is then derived as: 3rd International Conference on Education, Management and Computing Technology (ICEMCT 2016) © 2016. The authors Published by Atlantis Press 1388 (1) (1) dX aX dX μ + = (3) Where a is the grey figure andμ is the grey action. Assume thatα is a parameter vector whose value is to be assigned, a α μ   =     (4) By applying the least square method, it can be derived that: 1 ( ) T T B B B Yn α − = (5)

The Level of Consumption Prediction and Analysis Based on the Grey Prediction Model