The Android packing techniques were originally used to pack and conceal important information of the apps to prevent malicious developers from deconstructing the software logic. However, due to the lack of supervision, packing techniques have become the common methods for Android malwares to harden apps and circumvent virus detection engines in recent years. With obfuscation and encryption techniques, packing engines can alter the code structure of malwares and hide the malicious code to deceive and bypass the detection mechanisms, such as signature matching. For packing techniques, the packers are the agents created by packing engines and used to protect the softwares. Hence the packers pose a major challenge to the automated malware detection when researchers analyze a large collection of Android apps statically. It is necessary to identify packed samples in advance so that researchers can adopt different process procedures. To address this problem, we propose an intelligent AnDroid Packer Detection Framework called DroidPDF. It adopts a concise feature set that is resilient to obfuscation techniques. It also introduces weighted entropy to improve the detection effectiveness and achieves an average F1 Score of 0.9870.