This paper was aimed at discussing the information monitoring of animal husbandry based on the Internet of Things and wireless communication system. The breeding and health of animals in the breeding industry has always been a topic that people talk about. The advent of the wireless communication system has made monitoring and positioning technologies more and more simple. The wireless communication network technology is applied to the environmental monitoring of animal breeding farms, and a real-time reporting system is designed to pay attention to animal health in real time. This article focuses on the connection between the two. First, this article briefly describes the state of the wireless communication network and the aquaculture industry, furthermore explains the research methods, such as the livestock breeding environment monitoring system model, which needs to have the characteristics of humanization, fast and simple, easy to maintain, high reliability, compatibility, scalability, and intelligence, and designs related monitoring systems and hardware systems to integrate carbon dioxide, ammonia, and other gas sensors with temperature and humidity sensors to sense the environment. Next, this article shows the wireless communication network monitoring and positioning algorithm, namely, the TOA-based wireless communication positioning algorithm and the LTE prediction algorithm. The predicted time is used as the link weight, and the weight within the wide link cluster is defined according to the time threshold, making the link maintain stability for a short time to enhance the network topology. Then, this article conducts experiments based on ZigBee wireless communication network sensor combined with improved genetic algorithm in the temperature and humidity test of farms, designs the environmental monitoring system, improves the algorithm, and cooperates with experiments and analysis to verify the feasibility and apply it to the temperature and humidity test of the livestock farm. The results are good, and the temperature and humidity errors are reduced by 88.28% and 84.21%, respectively. It has a certain degree of guidance. Finally, it is discussed and summarized. It can be seen that the system and algorithm designed in this paper have a good prospect in the development of animal husbandry. However, this algorithm takes a long time and has a broader research space.