A new way to classify shape memory materials (SMMs) was proposed based on the underlying working mechanisms behind the shape memory effect (SME). Three categories, namely dual-state mechanism, dual-component mechanism, and partial-transition mechanism, were discussed in details. Based on this, the concept of advanced shape memory technology was proposed to enable the SME in materials, to design/synthesize new SMMs with tailored features, and to optimize the SME in materials. Previously, SME is considered as a unique behavior in some certain materials. In this study, based on ASMT, SME can be achieved in a range of materials, which are not the traditional SMMs. A generic 3-D model was developed to simulate the shape memory behavior in polymeric SMMs. This model was verified by a series of experiments. In addition, this model was applied for optimization of the SME. The thermo-/chemo-responsive SME in poly(methyl methacrylate) (PMMA) were systematically studied by experiments and simulation. Based on above study on the fundamentals, different surface patterning methods were developed to fabricate micro/nano-sized surface features, including well-controllable wrinkled patterns, PMMA microlens arrays, reversible surface patterns and 3-D surface patterns.