The clinicians diagnose Attention Deficit/Hyperactivity Disorder （ADHD） mainly based on on their subjective assessment， which lacks objective criteria to assist. To solve this problem， an automatic detection algorithm for ADHD based on speech pause and flatness was proposed. Firstly， the Frequency band Difference Energy Entropy Product （FDEEP） parameter was used to automatically locate the segment with voice from the speech and extract the speech pause features. Then， Transform Average Amplitude Squared Difference （TAASD） parameter was presented to calculate the voice multi-frequency and extract the flatness features. Finally， fusion features and the Support Vector Machine （SVM） classifier were combined to realize the automatic recognition of ADHD. The speech samples of the experiment were collected from 17 normal control children and 37 children with ADHD. Experimental results show that the proposed algorithm can effectively discriminate the normal children and children with ADHD， with an accuracy of 91.38%.