Chronic　disease　risk　prediction　based　on　electronic　health　record　(EHR)　is　an　important　research　direction　of　Internet　healthcare.　Current　studies　mainly　focused　on　developing　well-designed　deep　learning　models　to　predict　the　disease　risk　based　on　large-scale　and　high-quality　longitudinal　EHR　data.　However,　in　real-world　scenarios,　people＇s　medical　habits　and　low　prevalence　of　diseases　often　lead　to　few-shot　and　imbalanced　longitudinal　EHR　data.　This　has　become　an　urgent　challenge　for　chronic　disease　risk　prediction　based　on　EHR.　Aiming　at　this　challenge,　this　study　combines　EHR　based　pre-training　and　deep　reinforcement　learning　to　develop　a　novel　chronic　disease　risk　prediction　model　called　CDR-Detector.　The　model　adopts　the　Q-learning　architecture　with　a　custom　reward　function.　In　order　to　improve　the　few-shot　learning　ability　of　model,　a　self-adaptive　EHR　based　pre-training　model　with　two　new　pre-training　tasks　is　developed　to　mine　valuable　dependencies　from　single-visit　EHR　data.　In　order　to　solve　the　problem　of　data　imbalance,　a　dual　experience　replay　strategy　is　realized　to　help　the　model　select　representative　data　samples　and　accelerate　model　convergence　on　the　imbalanced　EHR　data.　A　group　of　experiments　have　been　conducted　on　real　personal　physical　examination　data.　Experimental　results　show　that,　compared　with　the　existing　state-of-art　methods,　the　proposed　CDR-Detector　has　better　accuracy　and　robustness　on　the　few-shot　and　imbalanced　EHR　data.