Nowadays consumer online reviews are becoming more and more important for enterprise decision-making. While the existing research seldom discussed review data from a dynamic perspective, especially ignored consumers' attention change during the product life cycle. To study whether there are dynamic changes and the characteristics of changes in the attention degree of consumers in each phase of the product life cycle, this paper coded a specific node program to collect the online reviews data of the four mobile phones in the entire product life cycle and used python's Chinese automatic word segmentation tool library to segment each word and count word frequency, and then a stepwise regression method was used to analyze the dynamic changes of consumer attention. The paper finds that consumers’ attention on logistics and products presented in online reviews show a downward trend, and the attention on brands shows an upward trend; There is no obvious change in the attention degree on services, prices, and promotion; On the different dimensions of products, there is a significant difference in the attention degree. The research results broad the research ideas of online reviews, provide decision-making basis for enterprises to grasp the characteristics of consumers at different stages and to formulate production and marketing strategies.