Online shopping is extremely popular and gradually replacing traditional shopping. Online markets are increasingly appearing and becoming very popular after the COVID-19 pandemic phenomenon worldwide. This article focuses on changes in customers' online shopping behavior amid the COVID-19 pandemic. We conduct systematic scientific analysis and visualization of 407 Scopus-indexed articles from 2019 to 2024. The five-year timeframe strikes a balance between capturing meaningful trends and practicality in data collection and analysis considering the context of the COVID pandemic. N-gram analysis identifies the terms to show the development trends of online shopping consumer behavior over the years. Based on prominent keywords, top authors, institutions, journals in the field, top author keywords, co-author networks, co-keywords, and top articles are also analyzed. The relations among top keywords, top authors and top journals are conducted by the Sankey diagram analysis. The research conducts document-term-matrix (DTM) analysis to identify seven key clusters, research hotspots, and emerging trends in the bibliometric network. This study can guide further research when artificial intelligence technology integrated into internet and mobile platforms is growing strongly. The obtained research results can serve policymakers, researchers, and practitioners to apply high technology to increase consumer experience in online shopping behavior.