In recent years, brain-controlled intelligent wheelchairs have received extensive attention, which combines the accessibility of the Brain-computer Interface (BCI) system with the intelligence of wheelchairs. However, current brain-controlled wheelchairs are always operated in a fixed mode. The Electroencephalogram (EEG) signals with the fixed acquisition time are analyzed without considering the state of the user, which not only increases the risk of misoperation, but seriously reduces the information transfer rate of the system. To solve this problem, an adaptive control approach for intelligent wheelchair based on BCI combining with Quality of Operating (QoO) is proposed. Firstly, the influence of motor imagery signals with different time lengths in different states on classification accuracy was analyzed using tangent space Support Vector Machine (TSSVM) algorithm. Then, the definition of QoO was introduced, which was obtained by analyzing sample entropy and power spectral density (PSD) of four kinds of EEG activities, delta, theta, alpha and beta. Finally, the acquisition time of required EEG signals was adjusted according to the value of QoO. We constructed a brain-controlled wheelchair system and conducted real environmental experiments for 9 subjects using strategies, with and without adaptive control approach. The results show that the approach proposed in this paper can reduce the risk of misoperation and increase the information transfer rate on the premise of ensuring the classification performance during navigation in complex indoor environment.