Based　on　the　openness　and　accessibility　of　user　data,　personality　recognition　is　widely　used　in　personalized　recommendation,　intelligent　medicine,　natural　language　processing,　and　so　on.　Existing　approaches　usually　adopt　a　single　deep　learning　mechanism　to　extract　personality　information　from　user　data,　which　leads　to　semantic　loss　to　some　extent.　In　addition,　researchers　encode　scattered　user　posts　in　a　sequential　or　hierarchical　manner,　ignoring　the　connection　between　posts　and　the　unequal　value　of　different　posts　to　classification　tasks.　We　propose　a　hierarchical　hybrid　model　based　on　a　self-attention　mechanism,　namely　HMAttn-ECBiL,　to　fully　excavate　deep　semantic　information　horizontally　and　vertically.　Multiple　modules　composed　of　convolutional　neural　network　and　bi-directional　long　short-term　memory　encode　different　types　of　personality　representations　in　a　hierarchical　and　partitioned　manner,　which　pays　attention　to　the　contribution　of　different　words　in　posts　and　different　posts　to　personality　information　and　captures　the　dependencies　between　scattered　posts.　Moreover,　the　addition　of　a　word　embedding　module　effectively　makes　up　for　the　original　semantics　filtered　by　a　deep　neural　network.　We　verified　the　hybrid　model　on　the　MyPersonality　dataset.　The　experimental　results　showed　that　the　classification　performance　of　the　hybrid　model　exceeds　the　different　model　architectures　and　baseline　models,　and　the　average　accuracy　reached　72.01%.