Disclosed is a text-based major depressive disorder recognition method capable of improving the accuracy of recognizing major depressive disorder. The text-based major depressive disorder recognition method comprises: acquiring voice information of a user under test, and converting same into text information; converting the text information into a text embedding vector; using a major depressive disorder prediction model to perform prediction on the basis of the text embedding vector, and obtaining a prediction result, the major depressive disorder prediction model being obtained by using a long short-term memory (LSTM) model to train text embedding vector samples, the text embedding vector samples including major depressive disorder text embedding vector samples and non-major depressive disorder text embedding vector samples; and determining a target keyword contained in the text information; and determining whether the user under test is affected with major depressive disorder according to a weighting result of the prediction result and a weight value corresponding thereto and the target keyword and a weight value corresponding thereto.
TEXT-BASED MAJOR DEPRESSIVE DISORDER RECOGNITION METHOD