In order to diagnose the position and degree of the damage of complex wingbox structures accurately, a method is presented by means of lifting wavelet packet decomposition, multi-sensor feature fusion theory and neural network pattern classification. Firstly, vibration signals gathered from sensors are decomposed by lifting wavelet packet transform. Secondly, the relative energy of decomposed frequency band is calculated. Thirdly, the input feature vectors of neural network classifier are built by fusing wavelet packet relative energy distributi on of these sensors. Finally, with the trained classifier, damage diagnosis and assessment are realized. The results indicate that: the frequency band energy decomposited by lifting wavelet packet transform could perfectly reflect the damage condition; the fused feature can make different information complementary, and reduce the uncertainty of damage detection information, so the damage information has much more precision and reliability, and the diagnosis accuracy can be improved.