Abstract: Abstract： Suicide is one of the most significant public health crises globally， surpassing the combined mortality rate of wars， homicides， and natural disasters. This study employs computer technology， machine learning， and deep learning methods to analyze social media texts that contain suicidal ideation， aiming to automatically extract the underlying causes of suicidal thoughts. The study investigates the impact of content features (such as words， parts of speech， dependency syntactic parsing) and emotional-psychological features (including linguistics， emotions， suicidal psychology) on the task of automatically extracting causes of suicidal ideation. Experimental results indicate that content features perform notably well and are the most significant and crucial factors among the features. Specifically， word features exhibit the best performance， while parts of speech and dependency syntactic parsing features are overshadowed by the inclusion of word features to some extent. In contrast， emotional-psychological features effectively complement and enhance content features. The expression of emotions， sentiments， or psychological aspects shows a positive correlation with the underlying causes of suicidal ideation.

Key words: Key words： suicide ideation, suicide ideation causes, social text, CRF, feature