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Based on the study of the ecosystem of the Huizhou-Qingyuan section of the Shantou-Zhanjiang Expressway, this paper is taking the sub-tropical mountain ecosystem as a reference. The author is selecting indicators from the three aspects of the ecosystem’s composition, function and habitat. The paper is applying the mathematical analysis methods such as component analysis and non-repeating two-factor analysis of variance to obtain an index system including the diagnosis of the degree of ecosystem degradation in the subtropical mountainous areas.

Based on the UAV remote sensing method combined with multi-source remote sensing data, the ecological background of the subtropical mountainous area was investigated based on the Huizhou to Qingyuan section of the Shantou-Zhanjiang Expressway. Ecological background evaluation indicators are the theoretical basis to support the analysis and evaluation of this article. The ecological indicators of roads in South China have selected five indicators: vegetation cover index, water environment index, soil erosion index, terrain slope index and land use type index.

This research examines the forecasting performance of wavelet neural network (WNN) model using published stock data obtained from Financial Times Stock Exchange (FTSE) Taiwan Stock Exchange (TWSE) 50 index, also known as Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), hereinafter referred to as Taiwan 50. Our WNN model uses particle swarm optimization (PSO) to choose the appropriate initial network values for different companies. The findings come with two advantages. First, the network initial values are automatically selected instead of being a constant. Second, threshold and training data percentage become constant values, because PSO assists with self-adjustment. We can achieve a success rate over 73% without the necessity to manually adjust parameter or create another math model.