With the development of AIOps, log-based failure diagnosis has become more and more important. However, this technique has a key bottleneck-the quality of logs. Today, the lack of log printing specifications and guidance for programmers is a key factor of poor log quality, thus the need of automatic logging decision so as to improve log quality is becoming urgent. This study focuses on automatic logging decision. Specifically, the aim is to propose a general logging point decision approach. Different from existing works, an automatic feature vector generation method is proposed based on program layered structure tree and reverse composition, which can be applied to software systems written in different programming languages. In addition, this study leverages transfer learning algorithms to achieve cross-component and cross-project logging point decision. The approach is evaluated on five popular open source software systems, namely, OpenStack, Tensorflow, SaltCloud, Hadoop, and Angel, in three typical application scenarios including software upgrading, new component development, and new project development. Results show that the proposed approach performs about 95% accuracy in Java projects and 70% accuracy in Python projects on average.