Few-shot image classification is a special task where the model learns to build correct concepts of categories from only a few examples. Due to the frequent occurrence of few-shot scenarios， it has aroused extensive research. However， most previous few-shot models process examples from different categories individually without considering inter-classes information. We propose a novel category-fusion network （CFN） to exploit the intra- and inter-classes information simultaneously by fusing the information of examples from different categories. The key part of CFN is the learning of a fusion map， that is， how to fuse the features in the sample to map out the network parameters. There is an important problem that whether the fusion mapping should change with different input examples. To explore this problem， we design three different modules：（1）the class-irrelevant module with a fixed mapping； （2）the semi-relevant module where the fusion mapping only depends on the target category whose knowledge is expected to be learned； （3）the fully-relevant module where the fusion mapping totally depends on input examples. Our network can build the concept of a certain category by learning from examples of several categories， and generates a classifier with fused information. The experiments show the effectiveness of our network in few-shot learning， which obtains 60.03% in accuray on the widely used MiniImageNet dataset.