Most　Simultaneous　Localization　and　Mapping　(SLAM)　systems　highly　rely　on　static　environments　assumption,　leading　to　low　pose　estimation　accuracy　in　dynamic　environments.　Dynamic　Visual　SLAM　(VSLAM)　methods　have　exhibited　remarkable　advantages　in　eliminating　negative　effects　of　dynamic　elements.　However,　most　current　methods,　only　built　on　traditional　indirect　VSLAM　using　hand-crafted　features,　are　still　inadequate　in　utilizing　and　processing　deep　features.　To　this　end,　this　paper　proposes　a　dynamic　VSLAM　algorithm　based　on　probability　screening　and　weighting　for　deep　features.　Specifically,　a　deep　feature　extraction　module　is　designed　to　generate　deep　features　leveraged　in　the　overall　pipeline.　Then,　probability　screening　and　weighting　scheme　is　proposed　for　processing　deep　features,　through　which　the　dynamic　deep　feature　points　are　eliminated　in　a　coarse-to-fine　manner　and　the　various　contributions　of　static　ones　is　distinguished.　Sufficient　quantitative　and　qualitative　experiments　prove　that　our　proposed　method　is　superior　to　other　counterparts　in　terms　of　localization　accuracy.　©　2024　Elsevier　Ltd