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Planning alternatives evaluation is often influenced by the evaluator’s background knowledge and preferences, and its objectivity is hard to guarantee. A comprehensive evaluation method combining Geographic Information System (GIS) with system analysis technology is proposed in this paper. Using a land use issue in America as an example, GIS was combined with Fuzzy Logic, and the Analytic Network Process (ANP) method was used to evaluate three planning alternatives. The evaluation value of each qualitative index was obtained by Fuzzy Comprehensive Evaluation, and the quantitative index value was calculated by GIS algorithms. A weighted hypermatrix of ANP network was constructed to reveal the overall relative importance weight of alternatives. The results indicate that, in this case study, the factor weight rankings that influenced the selection of the land use alternative are Ecological factors (above 40%), socioeconomic factors (30%), Physical and Chemical factors (10–17%), and cumulative related factors (10%). In the long run, choices of planning alternatives will greatly affect the natural environment, the physical and chemical environment, and the social economy. The results indicate planners have to pay attention to a wide range of both qualitative and quantitative factors as much as possible in land use decisions. This study illustrates how the GIS-ANP method combine qualitative and quantitative factors in planning for a comprehensive analysis, thus improving the objectivity of evaluating land use planning alternatives and determining the importance of influencing factors. Future work aims to optimize the evaluation index system of planning, and measure index values in a more precise way.

The stock market is an important part of the financial market. The stock price prediction based on the model has very important practical significance for individuals and enterprises. So this paper uses regression models to fit past stock prices and forecast their future volume. This paper uses the polynomial regression method to regression the stock price from 2012 to 2017, and then uses LSTM to predict the inventory. The data used in this paper is from 2012 to 2017. Training on the data of the past few years, predicting the output in 2017, and then comparing it with the actual output. After training, the result shows that the trend of the predicted volume is similar to the actual volume in 2017. Therefore, LSTM truly forecasts the stock volume.