Abstract: The study of public service motivation has been flourishing and evolving, yielding significant theoretical advancements, making it a prominent frontier in public management research. However, the mechanisms through which public service motivation translates into public service performance remain unclear, necessitating further investigation in the realm of human resource practices in public administration. In this study, employing the Apriori algorithm, machine learning unsupervised mining on relevant literature was conducted to uncover research gaps in the transformation of public service motivation. Based on this, from the perspective of social cognitive theory, the interrelationship among public service motivation, public service behavior, and public service performance, was systematically elucidated, establishing a logical framework that explores the interconnectedness and transformation between these three constructs. Finally, based on the logical transformation pathway, a creative analytical model was constructed at systemic network level, namely the “public service motivation - public service behavior - public service performance” triadic interactive development model, which aims to further develop and refine the theory of public service motivation, facilitating successful translation of theoretical insights into practical implementation within public service contexts.