Recently, device-free human behavior recognition has emerged as a prominent research area, achieving significant progress across various professional fields. Among these advancements, the rapid development of WiFi sensing has garnered considerable attention due to its potential to address complex applications. By combining WiFi sensing with deep learning methodologies, researchers have been able to tackle numerous challenges in this domain effectively. In this paper, we explore the use of Channel State Information (CSI) from WiFi systems to recognize human behavior in indoor environments. Human body movements affect wireless signal reflections, resulting in unique alterations in CSI features. This study focuses on leveraging these features to detect and classify human behaviors accurately. To accomplish this, CSI tools are utilized to collect reflected signals generated by various human activities. As raw CSI data often contains noise, we preprocess the data to filter out irrelevant information, enabling the extraction of meaningful features. Deep learning techniques are employed to classify these features, while deep learning models are constructed to improve recognition accuracy. Through these methods, human behavioral activities can be effectively identified. We conduct a series of experiments to ensure the models are trained on comprehensive datasets, facilitating robust behavior detection. The study also compares different sensing tasks, evaluating recognition accuracy, computational complexity, adaptability, and trade-offs related to model size. Furthermore, practical applications of CSI-based systems are discussed, along with critical considerations for implementing reliable behavior recognition systems. Finally, this paper addresses open challenges in behavior recognition applications based on CSI and provides suggestions for future research directions. These recommendations aim to enhance WiFi sensing capabilities and expand its applications. Through innovative use of CSI and deep learning, this paper seeks to contribute to the advancement of human behavior recognition technologies.