The global prevalence of non-suicidal self-harm (NSSI) among adolescents is becoming increasingly severe. Traditional mental health services are struggling to meet the growing social demand due to limited resource allocation and service accessibility. The rapid development of artificial intelligence (AI) technology provides a new technological path. The article systematically reviews the research progress of AI technology in adolescent NSSI, demonstrating key technologies such as machine learning, natural language processing, and deep learning in predicting NSSI risk prediction, emotion recognition and online intervention for adolescents. However, challenges remain, including algorithm bias, data privacy protection, model interpretability and ethical decision-making. Future research should focus on multi-disciplinary collaborative cooperation based on artificial intelligence to build a safe, effective and sustainable digital psychological intervention system, so as to provide innovative strategies and technical support for the early warning and intervention of NSSI behavior in adolescents.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |