As one of the projective drawing techniques, the House-Tree-Person test (HTP) has been widely used in psychological counseling. However, its validity in diagnosing mental health problems remains controversial. Here, we adopted two approaches to examine the validity of HTP in diagnosing mental health problems objectively. First, we summarized the diagnostic features reported in previous HTP studies and found no reliable association between the existing HTP indicators and mental health problems studied. Next, after obtaining HTP drawings and depression scores from 4196 Chinese children and adolescents (1890 females), we used the Deep Neural Networks (DNNs) to explore implicit features from entire HTP drawings that might have been missed in previous studies. We found that although the DNNs successfully learned to extract critical features of houses, trees, and persons in HTP drawings for object classification, it failed to classify the drawings of depressive individuals from those of non-depressive individuals. Taken together, our study casts doubts on the validity of the HTP in diagnosing mental health problems, and provides a practical paradigm of examining the validity of projective tests with deep learning.