The digital marketing has become more popular than ever. In order to realize the on-line behavior of the users and tackle the considerable web data generated by the users, the analysis of web browsing behavior has been drawn a lot of attention, which challenges researchers. Web usage mining (WUM) exploiting data mining, text mining, machine learning and statistic is a useful method to solve problems where the hidden knowledge can be extracted for better understanding various interests of web users. In this investigation, consumer browsing log data were collected at google analytics, and then unstructured browsing log was preprocessed into semi-structure text. For each session, all URLs were compacted as a sentence representing a consumer's browsing path, which was further performed by hierarchical neural network, and then renormalized by Hopfield neural network to obtain the stable weights between URLs. The final two visualizing graphs highlights two types of browsing patterns denoting the intent of two groups of consumers where the graph with more links reflects user as a visitor looking for interesting things and the plain graph reveals the user accessing website by using keywords at search engine as a consumer most likely taking action. The findings would make a significant contribution to the marketing strategy and understanding of the browsing behavior and intent of on-line users.