目的 研究具有连续状态空间的复杂包装产品信息在分布式网络实时数据库中的查询方式。方法 通过结合增强学习(EL)和自适应共振结构神经网络(ARS)，给出一种基于增强学习的自适应共振结构神经网络算法——ELARS2。在ARS2算法中引入增强学习的选择和评估方式，解决在ARS2算法中分类模式的查询问题。设计在存储空间中使用分布式网络实时数据库查询目标的仿真试验，并用2种ELARS2算法（TDARS2和QARS2算法）来实现，并与经典的EL算法进行对比。结果 2种ELARS2算法完成查询目标的平均时间显著小于经典的EL算法。结论 在2种ELARS2算法中，TDARS2比QARS2效果更好。

The work aims to study the way of query in a distributed network real-time database for complex packaging product information with continuous state space. Combined with the enhanced learning (EL) and the adaptive response structure (ARS) neural network, the ELARS2 algorithm, ARS neural network based on enhanced learning was given. The way of selection and assessment methods for EL were introduced in ARS2 algorithm, so as to solve the query problems of sort mode in ARS2 algorithm. The simulation test in which the distributed network real-time database in the memory space was used to inquire targets was designed and it was achieved with two ELARS2 algorithms (TDARS2 and QARS2). Such algorithms were compared with the classical EL algorithm. The average time to complete target query in two ELARS2 algorithms was significantly less than the classic EL algorithm. In two ELARS2 algorithms, the effect of TDARS2 is better than QARS2.