In the context of the digital economy, product review is an important source of information that reflects the real needs and emotions of consumers, and are also an important resource for exploring and analyzing product pain points. However, the diversity and complexity of a large amount of noise information and consumer emotional expression in product reviews challenge the identification and quantification of product pain points. In order to help enterprises accurately locate product pain points, improve product competitiveness, meet consumer needs, and innovate product optimization strategies, this paper builds a quantitative model of product pain points based on opinions. The model uses dependent syntax technology to formulate extraction rules and filtering rules for evaluation objects, emotional words and modifiers, and extracts a complete combination of opinions from product reviews. This paper defines the calculation method of the emotional value of the point of opinion combination and the emotional value of the evaluation object, and passes the correspondence between coarse-grained attributes and fine-grained features, a quantitative model of product pain points has been built. Empirical evidence proves that the model in this paper can effectively identify and quantify the pain points of the product, provide a scientific decision-making basis for enterprises to iterate and upgrade products and improve user experience.