Purpose　Because　of　short　life　cycle　and　fluctuating　greatly　in　total　sales　volumes　(TSV),　it　is　difficult　to　accumulate　enough　sales　data　and　mine　an　attribute　set　reflecting　the　common　needs　of　all　consumers　for　a　kind　of　experiential　product　with　short　life　cycle　(EPSLC).　Methods　for　predicting　TSV　of　long-life-cycle　products　may　not　be　suitable　for　EPSLC.　Furthermore,　point　prediction　cannot　obtain　satisfactory　prediction　results　because　information　available　before　production　is　inadequate.　Thus,　this　paper　aims　at　proposing　and　verifying　a　novel　interval　prediction　method　(IPM).　Design/methodology/approach　Because　interval　prediction　may　satisfy　requirements　of　preproduction　investment　decision-making,　interval　prediction　was　adopted,　and　then　the　prediction　difficult　was　converted　into　a　classification　problem.　The　classification　was　designed　by　comparing　similarities　in　attribute　relationship　patterns　between　a　new　EPSLC　and　existing　product　groups.　The　product　introduction　may　be　written　or　obtained　before　production　and　thus　was　designed　as　primary　source　information.　IPM　was　verified　by　using　data　of　crime　movies　released　in　China　from　2013　to　2017.　Findings　The　IPM　is　valid,　which　uses　product　introduction　as　input,　classifies　existing　products　into　three　groups　with　different　TSV　intervals,　mines　attribute　relationship　patterns　using　content　and　association　analyses　and　compares　similarities　in　attribute　relationship　patterns　-　to　predict　TSV　interval　of　a　new　EPSLC　before　production.　Originality/value　Different　from　other　studies,　the　IPM　uses　product　introduction　to　mine　attribute　relationship　patterns　and　compares　similarities　in　attribute　relationship　patterns　to　predict　the　interval　values.　It　has　a　strong　applicability　in　data　content　and　structure　and　may　realize　rolling　prediction.