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Gm(1,1) model is the simplest and also the most widely used grey prediction model. However, the manual computation cost is so very large, and cooperation of related calculation software is relatively complicated, so the performability is not that high. In cases of utilizing Java language to compile Gm(1,1) model desktop and running the software, the installation is convenient and operation is simple, so that the manual computation cost is reduced, which makes Gm(1,1) more effectively applied in terms of research on development tendency of marine economy and other aspects. A large number of factors affect the prediction of economic trend. There are either certainty factors or uncertainty factors (such as natural disasters) that cannot be carried out quantitative analysis directly. The grey system thought regards the development progress of marine economy as a grey system, and then predicts the economic trend. The existing software that could proceed grey prediction are Excel, Matlab, DPS, SAS etc. But all these software are complicated to operate, and performability is not high, so it is difficult for users who are not familiar with the software but in need of a quick Gm(1,1) prediction results. This paper uses Java language to compile Gm(1,1) model desktop software through detailed analysis of Gm(1,1) prediction principle, and realizes the quick installation and calculation function of Gm(1,1) prediction software. Furthermore, it verifies the operability and accuracy of the model, and the practicability of application by examples. Principle of Gm (1, 1) model In 1982, Deng Julong, a Chinese famous scholar, created grey system theory aiming at uncertain system of small samples and poor information. Thereinto, grey methodology(Gm in short) did not require too much about data, and could weaken the randomness of system and elevate precision of prediction, which had become the widely used method in short-term prediction. And as the simplest grey prediction model, Gm(1,1) gained more popularity. Its basic principle is as following: (1) Data preprocessing There are n numbers in the original non-negative series as following: (0) (0) (0) [ (1), , (n) ] X x x =  . Proceed the accumulative production processing to the original series for one time, generating 1-AGO series: (1) (1) (1) (1) (1), (2), , ( ) X x x x n   =    , thereinto, (1) (0) (1) (1) x x = , (1) (0)

The Development of Prediction Software Gm(1,1) and its Application in Marine Economy Field