Stuck pipe，a common risk during drilling operations，severely restricts safe and efficient drilling. Traditional methods for diagnosing and handling stuck pipe incidents mostly rely on expert experience，mechanistic models，or supervised machine learning algorithms and suffer from issues such as diagnostic lag，high false alarms due to overfitting，strong subjectivity，and low adaptability. This paper proposes a method for diagnosing stuck pipe risks and recommending cases of countermeasures based on unsupervised learning and K-Nearest Neighbors （KNN），which has been implemented as a software system. The system comprises two core modules：stuck pipe risk diagnosis and stuck pipe incident handling. The stuck pipe risk diagnosis module employs the Isolation Forest algorithm to monitor and diagnose abnormal trends in key real-time downhole engineering parameters. The stuck pipe incident handling module utilizes a Case-Based Reasoning （CBR） algorithm based on KD-Tree and KNN to retrieve cases from a stuck pipe decision case library，returning the data of three most similar cases to the client-side to assist engineers in handling stuck pipe incidents. Based on the aforementioned，an intelligent downhole sticking diagnosis system based on case-based reasoning was designed and implemented. Tests show that the system successfully issues warnings approximately 5 minutes before an incident occurs，quickly retrieves three most similar historical handling cases，and pushes them to the interface. This developed system provides direct decision support for engineers.