Autoencoder feature extraction plateau
A convolutional autoencoder will only make sense if you work with images (2D signals) or time series (1D signals). Convolutions identify local patterns in data, if this is not the case in your data it will most likely not solve your problem.
Using pre-trained AE will only help, if it was trained on similar data. Similar data in this case does not refer to the data type, but rather to what the data represents. If you have an AE which was trained to compress images of cats it will not work well on images of chairs, because cats and chairs do not share the same features. Although if you like to compress images of dogs you could use the weights of the AE for cats as a starting point (Transfer Learning).
What kind of loss are you using? MSE or Cross-entropy? Speaking from my experience, using cross-entropy yields better results (although this is problem dependent). Another issue could be vanishing gradients which can happen in very deep networks and with activation functions like sigmoid. What you can do is reduce the depth of your network, replace the sigmoid with ReLU and maybe try a different optimizer.
In any case PCA is a safe bet. It's linear, deterministic, well studied and quicker to use than to train a NN. Whatever method you use, you can use PCA as benchmark to see if your method beats it. Although with the size of your data you may run into memory issues.