针对基本层次化目标识别计算模型缺乏明确学习概念和有效学习方法的问题，利用神经稀疏编码的学习规则，生成原型向量集合。通过模拟复杂细胞的感受野特性，实现层次化的稀疏编码过程，提出基于神经稀疏编码的层次目标特征提取算法。并利用简化的分类器设计，完成复杂场景下的广义目标识别问题。在Caltech-101数据库上进行实验对比，结果表明本文算法相对Serre计算模型在识别正确率上有较大提高，时间复杂度增加并不明显，且更加符合生物视觉机理。

To address the lack of explicit concepts and effective methods with learning in most hierarchical visual computational models, we propose a novel hierarchical generic object recognition sketch using neural sparse coding. Firstly, the learning strategies are embedded in a hierarchical system for generating several prototypes to model the characteristic of complex cell receptive fields. Secondly, based on a hierarchical sparse coding process, we present a hierarchical feature extraction method for generic object recognition. Finally, a simplified classifier is designed to achieve our goals in complex scenes according to the extracted robust features. Experiments on Caltech-101 demonstrate the effectiveness in our method and the more preferable results comparing with Serre’s show greater consistency in biological vision.