Abstract: In terms of multi-instance learning,it is difficult for the bag-space features to capture local information of the bag.And it is also difficult for instance-space features to capture global and structural information of the bag.We proposed a multi-instance learning method to fuse bag-space features with instance-space features to solve the above problems.Firstly,we established a graph model that described structural relations among instances in a bag.The graph model was transformed as an affinity matrix which could be used as the bag-space features.Secondly,we selected the instances in the positive bags.The features of the instances would be regarded as the instance-space features,if the correlation between those instances and the category of the positive bag was relatively strong.And we selected the instances in the negative bags.The features of the instances would be regarded as the instance-space features of the negative bags,if the correlation between those instances and the category of the positive bag was weaker.Finally,we used the Gaussian RBF kernel to map the bag-space features and the instance-space features to the same feature space.Then we used the feature fusion method based on the weight to fuse the two kinds of features in the same feature space.The experimental results on benchmark data set for multi-instance learning,public image data set and text data set show that the classification performance is improved by the proposed method.