Reconfigurable intelligent surfaces (RISs) are recognized as a promising key enabling technology for sixth-generation (6G) networks, offering effective optimization of network topology and enhancement of system performance. Conformal RISs, whose physical shape elastically adapts to the host surface, enable large-scale deployment of intelligent surfaces in a “stealthy” manner, thereby unlocking the full performance potential of RIS-assisted networks. However, modeling such conformal RISs at both the element and array levels remains challenging due to the anisotropic scattering characteristics of individual elements, necessitating a scattering field superposition approach. This leads to an inherent trade-off among scale, computational efficiency, and accuracy in electromagnetic (EM) performance analysis. Moreover, existing two-level (element- and array-level integrated) EM analysis methods for conformal RIS suffer from limited computational efficiency, failing to meet the stringent requirements of rapid adaptability and real-time reconfiguration intelligence. To address these challenges, this paper proposes a novel and efficient EM analysis framework for conformal RIS based on a near-field equivalent source array mechanism. Specifically, we first develop a three-dimensional (3D) equivalent periodic model that extends the applicability of the pattern multiplication principle and integrates both network port-based methods and active near-field subarray extrapolation techniques. Subsequently, we establish a theoretical linkage and fusion mechanism between the network port method and the construction of 3D periodic near-field equivalent sources. By unifying all active near-field data—originating from internal control ports at the element level and incident field ports at the array level—into a single equivalent near-field source array, our approach achieves seamless compatibility between element-level modeling and array-level computation. This enables an efficient construction methodology for the periodic equivalent model, thereby broadening the applicability of the pattern multiplication principle to holistic performance analysis of conformal metasurfaces. Furthermore, we incorporate an active near-field subarray extrapolation technique to account for inter-element coupling and other mutual interactions. By extending the application of the fast Fourier transform (FFT) within the near-field equivalence framework, we establish an integrated two-level efficient EM analysis method tailored for conformal RIS. In contrast to existing machine learning or deep learning-based approaches, the proposed method not only enhances the applicability of the pattern multiplication principle but also synergistically leverages FFT acceleration. It maintains compatibility between element-level modeling and array-level computation while being insensitive to the scale and environmental variations of conformal RIS. Consequently, the proposed method exhibits high computational efficiency, notable methodological and technical innovation, and superior expected performance.