The microphone data collected in aeroacoustic wind tunnel test contains not only desired aeroacoustic signal but also background noise generated by the jet or the valve of the wind tunnel, so the desired aeroacoustic characteristics is difficult to be highlighted due to the low Signal-to-Noise Ratio (SNR). Classical cross spectral matrix removal can only reduce the microphone self-noise, but its effect is limited for jet noise. Therefore, an Airflow Background Noise Suppression method based on the Ensemble Empirical Mode Decomposition (ABNSEEMD) is proposed to eliminate the influence of background noise on aeroacoustic field reconstruction. The new method uses EEMD to adaptively separate the background noise in microphone data, which has good practicability for increasing SNR of aeroacoustic signal. A localization experiment was conducted by using two loudspeakers in wind tunnel with 80 m/s velocity. Results show that proposed method can filter out the background noise more effectively and improve the SNR of the loudspeakers signal compared with spectral subtraction and cepstrum methods. Moreover, the aeroacoustic field produced by a NACA EPPLER 862 STRUT airfoil model was also measured and reconstructed. Delay-and-sum beamforming maps of aeroacoustic source were displayed after the background noise was suppressed, which further demonstrates the proposed method’s advantage.