Using the NodeIdentifyR algorithm (NIRA) in R programming language, this study aimed to identify potential intervention targets by simulating interventions. It examined how alleviating or aggravating specific symptoms impacts the activation of the overall depressive symptom network in Chinese adolescents. A total of 3347 valid participants aged 10 to 18 years were recruited from China (Mage = 13.79, SD = 2.56; 52.94% female). "Feel lonely", "sad mood", and "low spirit" were identified as central symptoms. "Feel happy" emerged as a potential treatment target for alleviating overall depressive symptoms, and "feel lonely" was identified as potential prevention target for addressing depressive symptoms in adolescents. These findings emphasize the significance of computational modeling in enhancing the understanding of psychopathology, offering insights and cost-effective recommendations for future interventions aimed at addressing depressive symptoms in adolescents.