An airline baggage feature perception network model was designed with PointNet++ as the benchmark and incorporating graph convolutional neural network and self-attention mechanism aiming at the problem that the configuration feature perception capability of airline baggage was inadequate under the demand of automatic baggage stacking handling. The local spatial attention module was introduced in the feature abstraction layer of the backbone network to extract associated spatial structure features of neighboring points in aviation baggage point cloud in order to perceive the intrinsic connection of its region feature space. Correlation between local features of airline baggage point cloud was learned through the global feature aggregation module to adaptively aggregate local features so as to form global contextual information. The recycling maxpooling layer was applied to recycle features from some discard points in the feature reduction process and collect baggage information at multiple levels, reducing information redundancy while retaining local and global feature activations with stark intensity. The experimental results showed that the average and overall accuracy of airline baggage classification were 94.68% and 96.32%, which were 6.53% and 5.07% improved over PointNet++, respectively. The airline baggage feature perception performance of the network model is better than other existing intelligent algorithms, which can provide accurate, reliable and effective input for airline baggage stacking space optimization and control.