The analysis of agricultural Internet of things product marketing by deep learning

This study aims to promote the development of agricultural Internet of Things (AIoT) products. Although the general recurrent neural network (RNN) provides a more scientific approach for market forecasting, the accuracy of forecasting varies greatly because market data are affected by many factors. RNN is analyzed combined with the characteristics of IoT products. Through the analysis of the advantages and disadvantages of previous prediction models, long short-term memory (LSTM) is used to optimize the system model. Additionally, the optimized LSTM prediction model is used for hyper-parameter analysis of IoT marketing products. The mean absolute error of the developed LSTM prediction model reaches 303.3112, and the root-mean-square error reaches 397.1752. The prediction trend presented by the designed LSTM prediction model is consistent with the experimental data, which proves that the model has high prediction accuracy. The model is used to analyze future sales of Agricultural IoT (AIoT) products. The strength, weaknesses, opportunities and threats analysis method is used to analyze the AIoT product manufacturers. The AIoT product company has developed a marketing strategy that is in line with the company's development. This strategy promotes the development of IoT agricultural products and modern agricultural production.