The foundational assumption on piecewise regression is that partial information on the segments is known beforehand. Without the assumption, the difficulty of regression is not merely analytical, but also computational. In this paper, we introduce adaptive moment methods to identify a partially piecewise linear regression, without need of the information on the segments. The new idea is motivated by our findings that the moment conditions of the model contain the information of homogenous parameter and the subgroup-averages of the heterogenous parameters. Thus, we directly use the moment conditions to construct the estimator of the homogenous parameter, and identify the subgroup-averages of the heterogenous parameters. The resulting estimator for homogeneous parameter has a simple expression, which is similar in form to the common least square estimator, and is adaptive to various sizes of subgroups of heterogenous parameters. Based on the subgroup moment estimators, the subgroups of heterogenous parameters can be identified through mean change-point detections or dimension-reduced informational approaches. The methods are much easier than the existing methods. Our approaches are further illustrated via simulation studies and are applied to non-performing loan model.