Production Efficiency Prediction of Pig Breeding Industry by Optimized LSTM Computer Algorithm under Environmental Regulation

The study aims to improve the economic income of pig breeding industry under environmental regulation and control the environmental pollution caused by pig breeding. Long short-term memory (LSTM) neural network combined with environmental regulation is proposed to forecast the price of live pigs, to reduce the cost of environmental pollution control and improve the production efficiency of pig breeding. Primarily, analyses are made on the industrial structure and pollution of pigs in China, and studies are carried out on the inevitability of large-scale and intensive pig breeding. Then, pig breeding and environmental pollution are coordinated under the environmental regulation. From the perspective of green total factor productivity, calculation is made on the profit of pig breeding and the cost of environmental pollution control. Next, the LSTM neural network is used to predict the price of live pigs, thus effectively controlling the scale of pig breeding and making timely decisions that conform to market rules. The results show that with the increase of feed and land prices, the advantages of large-scale pig breeding gradually become prominent, which leads to the small- and medium-sized scale farmers withdrawing from the market. Compared with other similar models, the designed model can better simulate the future trend of hog price, of which the prediction accuracy is over 80%. When combined with environmental regulations, the prediction accuracy of the model for different data sets reaches 83%, so the designed model can better predict the changing trend of the price of live pigs, thus improving the production efficiency of large-scale pig farmers.