As digital interfaces become increasingly prevalent, a series of ethical issues have surfaced, with dark patterns emerging as a key research focus. These manipulative design strategies are widely employed in User Interfaces (UI) with the primary aim of steering user behavior in favor of service providers, often at the expense of the users themselves. This paper aims to address three main challenges in the study of dark patterns: inconsistencies and incompleteness in classification, limitations of detection tools, and inadequacies in data comprehensiveness. In this paper, we introduce a comprehensive framework, called the Dark Pattern Analysis Framework (DPAF). Utilizing this framework, we construct a comprehensive and standardized taxonomy of dark patterns, encompassing 64 types, each labeled with its impact on users and the likely scenarios in which it appears, validated through an industry survey. Upon assessing the capabilities of detection tools and data completeness, we find that of all the dark patterns, the five mainstream detection tools can only identify 32, yielding a coverage rate of merely 50%. Even though the existing four datasets collectively contain 5,566 instances, they cover only 32 of all types of dark patterns, also resulting in a total coverage rate of 50%. The results discussed above suggest that there is still significant room for advancement in the field of dark mode recognition. Through this research, we not only deepen our understanding of dark pattern classification and detection tools, but also offer valuable insights for future research and practice in this field.