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To improve the material fire risk index (IFHI) forecast precision and accuracy, we raise a way that the prediction model consists of the genetic algorithm (GA) and BP neural network. There are many merits in this model. For example, the weights and thresholds of BP neural networks can be improved by the advantages that the genetic algorithm can reach the peak of the forecasting accuracy of the whole situation. And we pose MSE, MAE, and MAPE to measure the effectiveness. The result of MSE, MAE, and MAPE is 0.005843, 0.0183, 1.08158. The experimental results show that the IFHI of the polymer can be accurately predicted by GA-BP neural networks.

With the decline of the world economy, the world shipping market is facing a series of crises such as the reduction of supply, and these crises are also the dilemma faced by Chinese shipping companies. Through the analysis of the important index BDI of the international shipping market, this paper realizes the trend of the international shipping market since the crisis of financial integration, and based on this, forecasts the international shipping market. At the same time, we analyze the important index of China’s shipping industry, China’s coastal bulk freight index (CCBFI) and China’s export container freight index (CCFI), which gives us a clearer understanding of the domestic and international shipping market situation. The business model is already suitable for the current shipping market and has reached the edge of change. Finally, based on market analysis, this paper analyzes the advantages and disadvantages of China’s shipping companies, and puts forward targeted development and transformation opinions on the disadvantages.