Abstract: Due to the scarcity of kick data samples, variations in different drilling conditions, and differences in the response rules 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. By using the random forest (RF) algorithm, an intelligent recognition model for drilling conditions was established. This model combined 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). In addition, a method for early intelligent kick detection based on the variation trend of engineering parameters was proposed. The proposed method was validated by using kick data from three wells, and the results showed that the accuracy of the intelligent recognition model for drilling conditions was 96.5%. Compared to manual warning methods, the average early warning time is 8.3 min in advance. The research demonstrates that this method is suitable for kick detection throughout the 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.