The accurate classification of flower images is the prerequisite for flower plant management to artificial intelligence, how to use the machine to classify flowers automatically is the current hot issue to be solved. This paper first introduced the principle of attention mechanism and realization of spatial attention mechanism and channel attention mechanism, and then designed the embedding of the spatial attention module and channel attention model in Xception structure based on Xception. Final, the network was optimized by jointing Triplet Loss and Softmax Loss in the network loss layer ,to obtain a feature embedding space with high intra-class compactness and inter-class separation. This paper was experimented on two flower image data sets (Oxford 17 flowers and Oxford 102 flowers), the results show that the MLSAN, MLCAN, MLCSAN model proposed in this paper were 0.39%, 0.50%, and 0.72% higher on the Oxford 17 flowers dataset and 0.52%, 0.63% and 0.85% higher on the dataset Oxford 102 flowers dataset.

花卉图像的准确分类是花卉植物管理走向人工智能的前提，如何利用机器对花卉进行自动分类是当前亟待解决的热点问题。本文首先介绍了注意力机制的原理以及空间注意力机制和通道注意力机制的实现，然后基于Xception设计了空间注意力模块和通道注意力模型在Xception结构中的嵌入。最后，网络被优化了通过在网络损失层联合Triplet Loss和Softmax Loss，获得类内紧密度高、类间分离度高的特征嵌入空间。本文在两个花卉图像数据集（牛津17朵花和牛津102朵花）上进行实验，结果表明本文提出的MLSAN、MLCAN、MLCSAN模型在Oxford 17上分别高出0.39%、0.50%、0.72%花数据集和牛津 102 花数据集的 0.52%、0.63% 和 0.85% 高。