The invention relates to a DCNN (Deep
Convolutional Neural Network)-DNN (Deep Neural Network) and PV-SVM (
Paragraph Vector-
Support Vector Machine)-based multi-
modal depressive disorder
estimation and classification method. The method comprises the following steps: preprocessing audio and video features through a displacement range
histogram and an Opensmile tool, extracting
hidden layer abstract features of audio and video statistical features through a DCNN, performing depressive disorder
estimation through a DNN, performing high-dimensional
feature mapping on
textile information through a PV method, inputting an obtained high-dimensional feature expression into an SVM for
binary classification, connecting a depressive disorder
estimation result with a
binary classification result in series, inputting the whole into a random forests model for training, and performing a final depressive disorder classification task through the trained random forests model, namely judging a depressive disorder or a non depressive disorder. By the adoption of a DCNN model for extraction of the
hidden layer abstract features from a primary audio / video, an original high-dimensional feature is more compact, and included information is richer; therefore, the model is more effective, and the phenomenon of
overfitting caused by extremely high dimension of the feature is avoided.