Imitation　learning　algorithms　for　robotics　applications　require　sufficient　optimal　data　to　learn　well-performing　strategies.　State-of-the-art　approaches　utilize　pre-labeled　data　or　interaction　with　the　environment　to　filter　suboptimal　data,　which　is　time-consuming　and　laborious　in　reality.　In　this　paper,　we　propose　a　new　approach　that　avoids　manual　labeling　or　environment　interaction.　We　design　an　additional　discriminator　for　the　behavioral　cloning　approach　to　distinguish　the　optimal　and　suboptimal　data　in　order　to　influence　policy　learning　and　avoid　suboptimal　behaviors.　Within　this　framework,　we　design　a　new　imitation　learning　algorithm　that　utilizes　the　output　of　the　discriminator　as　weights　to　learn　efficiently　on　datasets　containing　suboptimal　data.　We　evaluate　the　performance　of　the　proposed　method　in　four　environments　and　compare　it　with　three　benchmark　methods.　The　results　illustrate　that　our　method　has　better　performance　when　dealing　with　datasets　containing　suboptimal　data.　The　method　we　proposed　can　distinguish　data　with　higher　values　in　the　dataset　and　enable　the　agent　to　learn　high-performance　policy　from　imperfect　demonstrations　or　a　small　amount　of　data.　©　2024　IEEE.