Object tracking has always been a key issue in the field of computer vision. The kernel correlation filtering (KCF) transforms the calculation from the time domain into the frequency domain by Fourier transform, which greatly simplifies the calculation, not only improves the tracking speed, but also greatly improves the tracking accuracy. This article addresses the problem of object tracking under complex conditions. On the premise of ensuring the real-time performance of the algorithm, the features, scales and model updating mechanism are improved on the basis of KCF: This paper proposes an multi-feature fusion algorithm in order to combine all the advantages from different features; This paper proposes an adaptive algorithm of classification tree scale, which can judge the size of target scale and find the position of optimal response; This paper proposes an algorithm of adaptive model updating strategy. The experimental results show that, the public dataset OTB-2013, the average tracking accuracy and the average success rate of the proposed method are 87.4% and 67.1% respectively, which means that it can track the target under complex conditions very well. The comprehensive performance of the proposed algorithm ranks second among the published tracking algorithms. Especially in the case of scale change, target occlusion and image blurring, the tracking accuracy and success rate of the proposed algorithm rank first.