Although current artificial intelligence (AI) systems, especially deep learning models, have made significant progress in multiple fields, they still face problems such as high energy consumption, low interpretability, and insufficient flexibility. In contrast, in vitro Biological Neural Networks (BNN) are based on real biological neurons. Through their unique dynamic plasticity and adaptive learning capabilities, they can complete complex information processing tasks with low power consumption, laying the foundation for AI. Energy efficiency, interpretability and flexibility lead to completely new solutions. In addition, in vitro BNN uses precise culture technology and advanced control methods to enable the function and structure of neural networks to be precisely controlled, thereby providing an experimental platform for understanding the working mechanism of the brain, optimizing intelligent computing systems, and advancing brain-computer interface technology. Although the current computing performance of BNN is still in its infancy, its characteristics provide important directions and ideas for the development of efficient intelligent computing systems in the future. In recent years, bio-intelligent computing based on in vitro BNN has made significant progress in culture technology, signal monitoring and application scenarios. Through breakthroughs in two-dimensional and three-dimensional culture technology, BNN can more realistically simulate the biological brain neural network structure and promote the optimization of neural network functions. At the same time, combining multiple input methods such as electrical, optical and chemical stimulation allows neuronal activity to be precisely controlled, providing a new experimental platform for task adaptation and complex information processing. In terms of applications, BNN has shown great potential in static tasks (such as speech recognition) and real-time interactive tasks (such as neurorobot control), especially in dynamic learning, complex pattern recognition, and real-time tasks. In addition, the low power consumption characteristics and adaptive capabilities of BNN provide new ideas for the development of intelligent computing systems. In the future, with the further integration of neuroscience and computing technology, biological intelligent computing based on BNN is expected to usher in revolutionary progress in the fields of brain-computer interfaces, intelligent computing systems, and brain-like computing. Research on biological intelligent computing based on in vitro BNN has shown great potential to break through the bottleneck of traditional artificial intelligence and has obvious advantages in dynamic adaptation and complex pattern recognition. However, despite the initial results, BNN still face challenges in terms of computational efficiency, scalability, and compatibility with traditional computing systems. Future research will focus on further improving system integration, solving technical bottlenecks, and promoting the combination of BNN with existing computing technologies. With the advancement of technology and the deepening of interdisciplinary research, it is expected to promote the widespread application and in-depth development of biological intelligent computing in AI.