A study and discussion on the development of the pet industry using convolutional neural networks (CNN) optimized by improved particle swarm optimization (PSO)

The pet industry has witnessed a remarkable growth trajectory in recent years, encompassing a wide range of subsectors including breeding, food, healthcare, supplies, grooming, training, and boarding. As living standards rise and emotional needs deepen, the status of pets within families has grown, driving the expansion of the pet industry's market size. However, this rapid development also brings numerous challenges, such as pet population management, food exports, market size, and the diversity and dynamism of market demands, which increase the difficulty of precise marketing and the rational allocation of resources. Traditional management and decision-making methods are becoming increasingly inadequate in addressing these complex issues. The application of Convolutional Neural Networks (CNN) optimized by Improved Particle Swarm Optimization (PSO), as an intelligent optimization algorithm, offers unique advantages in solving complex optimization problems. Researching and enhancing this method for the development of the pet industry holds significant theoretical and practical value.