Nowadays,　more　and　more　online　content　providers　are　offering　multiple　types　of　data　services.　To　provide　users　with　a　better　service　experience,　Quality　of　Experience　(QoE)　has　been　widely　used　in　the　delivery　quality　measurement　of　network　services.　How　to　accurately　measure　the　QoE　score　for　all　types　of　network　services　has　become　a　meaningful　but　difficult　problem.　To　solve　this　problem,　we　proposed　a　unified　QoE　scoring　framework　that　measures　the　user　experience　of　almost　all　types　of　network　services.　The　framework　first　uses　a　machine　learning　model　(random　forest)　to　classify　network　services,　then　selects　different　nonlinear　expressions　based　on　the　type　of　service　and　comprehensively　calculates　the　QoE　score　through　the　Quality　of　Service　(QOS)　metrics　including　transmission　delay,　packet　loss　rate,　and　throughput　rate.　Experiment　results　show　that　the　proposed　method　has　the　ability　to　be　applied　on　almost　all　the　types　of　network　traffic,　and　it　achieves　better　QoE　assessment　accuracy　than　other　works.　©　2019　by　the　authors.