Abstract: Due to factors such as the scarcity of kick data samples, variations in different drilling conditions, and the differences in the response patterns of drilling parameters during different stages after a kick, existing kick detection methods exhibit poor robustness and are difficult to apply throughout the drilling process. Therefore, a method for early intelligent kick detection based on the trend of engineering parameter changes is proposed. Using the Random Forest (RF) algorithm, an intelligent recognition model for drilling conditions is established. This model combines a physical model to eliminate the impact of drilling conditions on kick characteristic parameters, extracting and integrating the trend values of these parameters to construct an adaptive calculation method for the Kick Risk Index (KRI). The proposed method was validated using kick data from three wells, and the results show that the accuracy of the intelligent recognition model for drilling conditions is 96.5%. Compared to manual warning methods, the average early warning time is 8.3 min. The study demonstrates that this method is suitable for kick detection throughout the entire drilling process and for scenarios where some characteristic parameter measurements may fail. It has significant guiding implications for ensuring drilling safety and shortening drilling cycles.