Aimed the rapid growth of the digital economy, major internet platforms increasingly use big data and algorithms for differential pricing. "Big-data-driven price discrimination," as a problematic form of differential pricing, has gradually become widespread. Platforms construct user profiles based on personal information, consumption levels, device models, and browsing history to estimate users' maximum willingness to pay, enabling personalized pricing, or "different prices for different users." This practice undermines substantive fairness in online transactions and seriously infringes upon consumers' right to fair transactions. China has established a basic regulatory framework through laws and regulations including the Law of the People's Republic of China on the Protection of Consumer Rights and Interests, the Personal Information Protection Law of the People's Republic of China, and the Rules on Price Conduct of Internet Platforms. Yet enforcement lags behind evolving platform practices. Key challenges include: vague legal standards for identifying unlawful pricing, opacity and complexity of algorithmic decision-making, consumers' difficulty meeting evidentiary burdens, and high costs of redress. This paper analyzes how big-data-driven price discrimination operates, identifies gaps and delays in current law through case review, and maps consumer obstacles. It proposes four targeted improvements: refining legal rules to clarify applicable standards, promoting algorithm transparency and strengthening full-process regulation, supporting consumer rights protection and redress, and balancing business efficiency with transactional fairness. The findings provide actionable insights for regulating algorithmic pricing and improving governance in the digital economy.