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Indoor localization finds wide-ranging applications in user navigation and intelligent building systems. Nevertheless, signal interference within complex indoor environments and challenges regarding localization generalization in multi - building and multi - floor scenarios have restricted the performance of traditional positioning methods based on WiFi fingerprinting. To tackle these issues, this paper presents the SE - MTCAELoc model, a multi - task convolutional autoencoder approach that integrates a squeeze - excitation (SE) attention mechanism for indoor positioning. Firstly, the method pre - processes WiFi Received Signal Strength (RSSI) data. In the UJIIndoorLoc dataset, the 520 - dimensional RSSI features are extended to 576 dimensions and reshaped into a 24×24 matrix. Meanwhile, Gaussian noise is introduced to enhance the robustness of the data. Subsequently, an integrated SE module combined with a convolutional autoencoder (CAE) is constructed. This module aggregates channel spatial information through squeezing operations and learns channel weights via excitation operations. It dynamically enhances key positioning features and suppresses noise. Finally, a multi - task learning architecture based on the SE - CAE encoder is established to jointly optimize building classification, floor classification, and coordinate regression tasks. Priority balancing is achieved using weighted losses (0.1 for building classification, 0.2 for floor classification, and 0.7 for coordinate regression). Experimental results on the UJIIndoorLoc dataset indicate that the accuracy of building classification reaches 99.57%, the accuracy of floor classification is 98.57%, and the mean absolute error (MAE) for coordinate regression is 5.23 meters. On the TUT2018 dataset, the floor classification accuracy attains 98.13%, with an MAE of 6.16 meters. These results suggest that the SE - MTCAELoc model can effectively enhance the localization accuracy and generalization ability in complex indoor scenarios and meet the localization requirements of multiple scenarios.